{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "6f6ce2ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import Data_Align\n",
    "import importlib\n",
    "importlib.reload(Data_Align)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# import typing\n",
    "# from numpy.lib.histograms import _hist_bin_sqrt\n",
    "# from numpy.polynomial import Polynomial\n",
    "# from pprint import pprint\n",
    "# # import pypg\n",
    "# from sklearn.metrics import pairwise_distances\n",
    "# from sklearn.cluster import AgglomerativeClustering\n",
    "# import scipy\n",
    "# from scipy.linalg import hankel\n",
    "# from scipy.optimize import minimize\n",
    "# from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "# import padasip as pa\n",
    "import nolds\n",
    "import neurokit2 as nk\n",
    "# import astropy\n",
    "# import antropy\n",
    "# import entropy\n",
    "# import EntropyHub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "505cb134",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-31 14:21:03,419 - INFO - ===== 开始处理文件: D:\\multimode_database\\sub13-songweifeng\\gateway_eegdata_csv\\songweifeng_1752718597_1752720246_precedent_stage_signals_1.csv =====\n",
      "2025-07-31 14:21:04,643 - INFO - 成功加载数据，共 823000 行，列名：['timestep', 'device_info_id', 'eeg_channel1', 'eeg_channel2', 'eeg_channel3', 'eeg_channel4', 'eog_channel1', 'eog_channel2', 'trigger_channel', 'ppg_red', 'ppg_infrared', 'imu_acc_x', 'imu_acc_y', 'imu_acc_z', 'imu_gyro_x', 'imu_gyro_y', 'imu_gyro_z', 'image_path', 'label']\n",
      "2025-07-31 14:21:05,326 - INFO - 检测到起始trigger: 1，时间戳: 1752718653409.0ms\n",
      "2025-07-31 14:21:06,312 - INFO - 检测到结束trigger: 10，时间戳: 1752718773461.0ms\n",
      "2025-07-31 14:21:06,312 - INFO - 事件匹配成功: 1 -> 10，时间范围: 1752718653409.0ms ~ 1752718773461.0ms，持续 120052.0ms\n",
      "2025-07-31 14:21:06,569 - INFO - 检测到起始trigger: 6，时间戳: 1752718803493.0ms\n",
      "2025-07-31 14:21:08,045 - INFO - 检测到结束trigger: 60，时间戳: 1752718983553.0ms\n",
      "2025-07-31 14:21:08,045 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752718803493.0ms ~ 1752718983553.0ms，持续 180060.0ms\n",
      "2025-07-31 14:21:08,336 - INFO - 检测到起始trigger: 3，时间戳: 1752719019617.0ms\n",
      "2025-07-31 14:21:09,308 - INFO - 检测到结束trigger: 30，时间戳: 1752719139627.0ms\n",
      "2025-07-31 14:21:09,308 - INFO - 事件匹配成功: 3 -> 30，时间范围: 1752719019617.0ms ~ 1752719139627.0ms，持续 120010.0ms\n",
      "2025-07-31 14:21:09,542 - INFO - 检测到起始trigger: 6，时间戳: 1752719167777.0ms\n",
      "2025-07-31 14:21:11,028 - INFO - 检测到结束trigger: 60，时间戳: 1752719347831.0ms\n",
      "2025-07-31 14:21:11,030 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752719167777.0ms ~ 1752719347831.0ms，持续 180054.0ms\n",
      "2025-07-31 14:21:11,244 - INFO - 检测到起始trigger: 22，时间戳: 1752719371927.0ms\n",
      "2025-07-31 14:21:11,558 - INFO - 检测到结束trigger: 20，时间戳: 1752719409793.0ms\n",
      "2025-07-31 14:21:11,559 - INFO - 事件匹配成功: 22 -> 20，时间范围: 1752719371927.0ms ~ 1752719409793.0ms，持续 37866.0ms\n",
      "2025-07-31 14:21:11,637 - INFO - 检测到起始trigger: 24，时间戳: 1752719419045.0ms\n",
      "2025-07-31 14:21:11,891 - INFO - 检测到结束trigger: 20，时间戳: 1752719450139.0ms\n",
      "2025-07-31 14:21:11,892 - INFO - 事件匹配成功: 24 -> 20，时间范围: 1752719419045.0ms ~ 1752719450139.0ms，持续 31094.0ms\n",
      "2025-07-31 14:21:11,982 - INFO - 检测到起始trigger: 21，时间戳: 1752719458929.0ms\n",
      "2025-07-31 14:21:12,208 - INFO - 检测到结束trigger: 20，时间戳: 1752719487267.0ms\n",
      "2025-07-31 14:21:12,209 - INFO - 事件匹配成功: 21 -> 20，时间范围: 1752719458929.0ms ~ 1752719487267.0ms，持续 28338.0ms\n",
      "2025-07-31 14:21:12,290 - INFO - 检测到起始trigger: 23，时间戳: 1752719496413.0ms\n",
      "2025-07-31 14:21:12,553 - INFO - 检测到结束trigger: 20，时间戳: 1752719528033.0ms\n",
      "2025-07-31 14:21:12,553 - INFO - 事件匹配成功: 23 -> 20，时间范围: 1752719496413.0ms ~ 1752719528033.0ms，持续 31620.0ms\n",
      "2025-07-31 14:21:12,695 - INFO - 检测到起始trigger: 6，时间戳: 1752719544395.0ms\n",
      "2025-07-31 14:21:14,209 - INFO - 检测到结束trigger: 60，时间戳: 1752719724447.0ms\n",
      "2025-07-31 14:21:14,209 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752719544395.0ms ~ 1752719724447.0ms，持续 180052.0ms\n",
      "2025-07-31 14:21:14,389 - INFO - 检测到起始trigger: 5，时间戳: 1752719745123.0ms\n",
      "2025-07-31 14:21:15,367 - INFO - 检测到结束trigger: 50，时间戳: 1752719865181.0ms\n",
      "2025-07-31 14:21:15,367 - INFO - 事件匹配成功: 5 -> 50，时间范围: 1752719745123.0ms ~ 1752719865181.0ms，持续 120058.0ms\n",
      "2025-07-31 14:21:15,522 - INFO - 检测到起始trigger: 6，时间戳: 1752719884695.0ms\n",
      "2025-07-31 14:21:16,982 - INFO - 检测到结束trigger: 60，时间戳: 1752720064747.0ms\n",
      "2025-07-31 14:21:16,983 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752719884695.0ms ~ 1752720064747.0ms，持续 180052.0ms\n",
      "2025-07-31 14:21:17,296 - INFO - 检测到起始trigger: 4，时间戳: 1752720102923.0ms\n",
      "2025-07-31 14:21:18,274 - INFO - 检测到结束trigger: 40，时间戳: 1752720222981.0ms\n",
      "2025-07-31 14:21:18,274 - INFO - 事件匹配成功: 4 -> 40，时间范围: 1752720102923.0ms ~ 1752720222981.0ms，持续 120058.0ms\n",
      "2025-07-31 14:21:18,460 - INFO - 文件处理完成，共检测到 12 个有效事件\n",
      "2025-07-31 14:21:18,460 - INFO - ===== 处理结束 =====\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功读取文件: aligned_10.csv，数据形状: (285583, 5)\n",
      "成功读取文件: aligned_11.csv，数据形状: (360191, 5)\n",
      "成功读取文件: aligned_12.csv，数据形状: (127251, 5)\n",
      "所有文件合并完成，总数据形状: (773025, 5)\n",
      "成功读取文件: aligned_10.csv，数据形状: (286000, 7)\n",
      "成功读取文件: aligned_11.csv，数据形状: (360179, 7)\n",
      "成功读取文件: aligned_12.csv，数据形状: (127428, 7)\n",
      "所有文件合并完成，总数据形状: (773607, 7)\n",
      "成功读取文件: 10.csv，数据形状: (282, 2)\n",
      "成功读取文件: 11.csv，数据形状: (359, 2)\n",
      "成功读取文件: 12.csv，数据形状: (127, 2)\n",
      "所有文件合并完成，总数据形状: (768, 2)\n",
      "成功读取文件: aligned_10.csv，数据形状: (3727, 2)\n",
      "成功读取文件: aligned_11.csv，数据形状: (4643, 2)\n",
      "成功读取文件: aligned_12.csv，数据形状: (1634, 2)\n",
      "所有文件合并完成，总数据形状: (10004, 2)\n",
      "帧率抖动: 0.01 ms\n",
      "丢包率: 0.0\n",
      "真实采样率: 100.0\n",
      "1752718599607.0\n",
      "对齐后脑eeg数据：(823000, 19)\n",
      "对齐后脑ppg数据：(164600, 5)\n",
      "对齐后脑电采集时间：1646.0\n",
      "对齐后PPG的采集时间：1646.0\n",
      "ppg开始时间1752718653410.0ms, imu开始时间1752718653410.0ms\n",
      "ppg开始时间1752718803500.0ms, imu开始时间1752718803500.0ms\n",
      "ppg开始时间1752719019620.0ms, imu开始时间1752719019620.0ms\n",
      "ppg开始时间1752719167780.0ms, imu开始时间1752719167780.0ms\n",
      "ppg开始时间1752719371930.0ms, imu开始时间1752719371930.0ms\n",
      "ppg开始时间1752719419050.0ms, imu开始时间1752719419050.0ms\n",
      "ppg开始时间1752719458930.0ms, imu开始时间1752719458930.0ms\n",
      "ppg开始时间1752719496420.0ms, imu开始时间1752719496420.0ms\n",
      "ppg开始时间1752719544400.0ms, imu开始时间1752719544400.0ms\n",
      "ppg开始时间1752719745130.0ms, imu开始时间1752719745130.0ms\n",
      "ppg开始时间1752719884700.0ms, imu开始时间1752719884700.0ms\n",
      "ppg开始时间1752720102930.0ms, imu开始时间1752720102930.0ms\n",
      "\n",
      "事件1：\n",
      "  触发器: 1->10\n",
      "  时间范围: 1752718653409.0ms - 1752718773461.0ms\n",
      "  提取到EEG样本数: 60027\n",
      "  提取到PPG样本数: 12006\n",
      "  提取到IMU样本数: 12006\n",
      "\n",
      "事件2：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752718803493.0ms - 1752718983553.0ms\n",
      "  提取到EEG样本数: 90031\n",
      "  提取到PPG样本数: 18006\n",
      "  提取到IMU样本数: 18006\n",
      "\n",
      "事件3：\n",
      "  触发器: 3->30\n",
      "  时间范围: 1752719019617.0ms - 1752719139627.0ms\n",
      "  提取到EEG样本数: 60006\n",
      "  提取到PPG样本数: 12001\n",
      "  提取到IMU样本数: 12001\n",
      "\n",
      "事件4：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752719167777.0ms - 1752719347831.0ms\n",
      "  提取到EEG样本数: 90028\n",
      "  提取到PPG样本数: 18006\n",
      "  提取到IMU样本数: 18006\n",
      "\n",
      "事件5：\n",
      "  触发器: 22->20\n",
      "  时间范围: 1752719371927.0ms - 1752719409793.0ms\n",
      "  提取到EEG样本数: 18934\n",
      "  提取到PPG样本数: 3787\n",
      "  提取到IMU样本数: 3787\n",
      "\n",
      "事件6：\n",
      "  触发器: 24->20\n",
      "  时间范围: 1752719419045.0ms - 1752719450139.0ms\n",
      "  提取到EEG样本数: 15548\n",
      "  提取到PPG样本数: 3109\n",
      "  提取到IMU样本数: 3109\n",
      "\n",
      "事件7：\n",
      "  触发器: 21->20\n",
      "  时间范围: 1752719458929.0ms - 1752719487267.0ms\n",
      "  提取到EEG样本数: 14170\n",
      "  提取到PPG样本数: 2834\n",
      "  提取到IMU样本数: 2834\n",
      "\n",
      "事件8：\n",
      "  触发器: 23->20\n",
      "  时间范围: 1752719496413.0ms - 1752719528033.0ms\n",
      "  提取到EEG样本数: 15811\n",
      "  提取到PPG样本数: 3162\n",
      "  提取到IMU样本数: 3162\n",
      "\n",
      "事件9：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752719544395.0ms - 1752719724447.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件10：\n",
      "  触发器: 5->50\n",
      "  时间范围: 1752719745123.0ms - 1752719865181.0ms\n",
      "  提取到EEG样本数: 60030\n",
      "  提取到PPG样本数: 12006\n",
      "  提取到IMU样本数: 12006\n",
      "\n",
      "事件11：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752719884695.0ms - 1752720064747.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件12：\n",
      "  触发器: 4->40\n",
      "  时间范围: 1752720102923.0ms - 1752720222981.0ms\n",
      "  提取到EEG样本数: 60030\n",
      "  提取到PPG样本数: 12006\n",
      "  提取到IMU样本数: 12006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-31 14:21:21,392 - INFO - ===== 开始处理文件: D:\\multimode_database\\sub13-songweifeng\\gateway_eegdata_csv\\songweifeng_1752724386_1752725875_posterior_stage_signals_1.csv =====\n",
      "2025-07-31 14:21:22,517 - INFO - 成功加载数据，共 743000 行，列名：['timestep', 'device_info_id', 'eeg_channel1', 'eeg_channel2', 'eeg_channel3', 'eeg_channel4', 'eog_channel1', 'eog_channel2', 'trigger_channel', 'ppg_red', 'ppg_infrared', 'imu_acc_x', 'imu_acc_y', 'imu_acc_z', 'imu_gyro_x', 'imu_gyro_y', 'imu_gyro_z', 'image_path', 'label']\n",
      "2025-07-31 14:21:22,786 - INFO - 检测到起始trigger: 3，时间戳: 1752724400080.0ms\n",
      "2025-07-31 14:21:23,802 - INFO - 检测到结束trigger: 30，时间戳: 1752724520082.0ms\n",
      "2025-07-31 14:21:23,802 - INFO - 事件匹配成功: 3 -> 30，时间范围: 1752724400080.0ms ~ 1752724520082.0ms，持续 120002.0ms\n",
      "2025-07-31 14:21:23,968 - INFO - 检测到起始trigger: 6，时间戳: 1752724539670.0ms\n",
      "2025-07-31 14:21:25,494 - INFO - 检测到结束trigger: 60，时间戳: 1752724719722.0ms\n",
      "2025-07-31 14:21:25,495 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752724539670.0ms ~ 1752724719722.0ms，持续 180052.0ms\n",
      "2025-07-31 14:21:25,622 - INFO - 检测到起始trigger: 25，时间戳: 1752724734320.0ms\n",
      "2025-07-31 14:21:25,835 - INFO - 检测到结束trigger: 20，时间戳: 1752724759646.0ms\n",
      "2025-07-31 14:21:25,836 - INFO - 事件匹配成功: 25 -> 20，时间范围: 1752724734320.0ms ~ 1752724759646.0ms，持续 25326.0ms\n",
      "2025-07-31 14:21:25,921 - INFO - 检测到起始trigger: 27，时间戳: 1752724769588.0ms\n",
      "2025-07-31 14:21:26,134 - INFO - 检测到结束trigger: 20，时间戳: 1752724795298.0ms\n",
      "2025-07-31 14:21:26,135 - INFO - 事件匹配成功: 27 -> 20，时间范围: 1752724769588.0ms ~ 1752724795298.0ms，持续 25710.0ms\n",
      "2025-07-31 14:21:26,209 - INFO - 检测到起始trigger: 28，时间戳: 1752724804286.0ms\n",
      "2025-07-31 14:21:26,452 - INFO - 检测到结束trigger: 20，时间戳: 1752724834772.0ms\n",
      "2025-07-31 14:21:26,453 - INFO - 事件匹配成功: 28 -> 20，时间范围: 1752724804286.0ms ~ 1752724834772.0ms，持续 30486.0ms\n",
      "2025-07-31 14:21:26,535 - INFO - 检测到起始trigger: 26，时间戳: 1752724843870.0ms\n",
      "2025-07-31 14:21:26,759 - INFO - 检测到结束trigger: 20，时间戳: 1752724870612.0ms\n",
      "2025-07-31 14:21:26,760 - INFO - 事件匹配成功: 26 -> 20，时间范围: 1752724843870.0ms ~ 1752724870612.0ms，持续 26742.0ms\n",
      "2025-07-31 14:21:26,891 - INFO - 检测到起始trigger: 6，时间戳: 1752724885220.0ms\n",
      "2025-07-31 14:21:28,427 - INFO - 检测到结束trigger: 60，时间戳: 1752725065272.0ms\n",
      "2025-07-31 14:21:28,428 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752724885220.0ms ~ 1752725065272.0ms，持续 180052.0ms\n",
      "2025-07-31 14:21:28,532 - INFO - 检测到起始trigger: 1，时间戳: 1752725078270.0ms\n",
      "2025-07-31 14:21:29,565 - INFO - 检测到结束trigger: 10，时间戳: 1752725198322.0ms\n",
      "2025-07-31 14:21:29,565 - INFO - 事件匹配成功: 1 -> 10，时间范围: 1752725078270.0ms ~ 1752725198322.0ms，持续 120052.0ms\n",
      "2025-07-31 14:21:29,667 - INFO - 检测到起始trigger: 6，时间戳: 1752725209854.0ms\n",
      "2025-07-31 14:21:31,203 - INFO - 检测到结束trigger: 60，时间戳: 1752725389906.0ms\n",
      "2025-07-31 14:21:31,203 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752725209854.0ms ~ 1752725389906.0ms，持续 180052.0ms\n",
      "2025-07-31 14:21:31,404 - INFO - 检测到起始trigger: 4，时间戳: 1752725414030.0ms\n",
      "2025-07-31 14:21:32,394 - INFO - 检测到结束trigger: 40，时间戳: 1752725534090.0ms\n",
      "2025-07-31 14:21:32,395 - INFO - 事件匹配成功: 4 -> 40，时间范围: 1752725414030.0ms ~ 1752725534090.0ms，持续 120060.0ms\n",
      "2025-07-31 14:21:32,487 - INFO - 检测到起始trigger: 6，时间戳: 1752725545054.0ms\n",
      "2025-07-31 14:21:34,028 - INFO - 检测到结束trigger: 60，时间戳: 1752725725106.0ms\n",
      "2025-07-31 14:21:34,029 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752725545054.0ms ~ 1752725725106.0ms，持续 180052.0ms\n",
      "2025-07-31 14:21:34,142 - INFO - 检测到起始trigger: 5，时间戳: 1752725737582.0ms\n",
      "2025-07-31 14:21:35,157 - INFO - 检测到结束trigger: 50，时间戳: 1752725857640.0ms\n",
      "2025-07-31 14:21:35,159 - INFO - 事件匹配成功: 5 -> 50，时间范围: 1752725737582.0ms ~ 1752725857640.0ms，持续 120058.0ms\n",
      "2025-07-31 14:21:35,309 - INFO - 文件处理完成，共检测到 12 个有效事件\n",
      "2025-07-31 14:21:35,309 - INFO - ===== 处理结束 =====\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功读取文件: aligned_10.csv，数据形状: (285583, 5)\n",
      "成功读取文件: aligned_11.csv，数据形状: (360191, 5)\n",
      "成功读取文件: aligned_12.csv，数据形状: (127251, 5)\n",
      "所有文件合并完成，总数据形状: (773025, 5)\n",
      "成功读取文件: aligned_10.csv，数据形状: (286000, 7)\n",
      "成功读取文件: aligned_11.csv，数据形状: (360179, 7)\n",
      "成功读取文件: aligned_12.csv，数据形状: (127428, 7)\n",
      "所有文件合并完成，总数据形状: (773607, 7)\n",
      "成功读取文件: 10.csv，数据形状: (282, 2)\n",
      "成功读取文件: 11.csv，数据形状: (359, 2)\n",
      "成功读取文件: 12.csv，数据形状: (127, 2)\n",
      "所有文件合并完成，总数据形状: (768, 2)\n",
      "成功读取文件: aligned_10.csv，数据形状: (3727, 2)\n",
      "成功读取文件: aligned_11.csv，数据形状: (4643, 2)\n",
      "成功读取文件: aligned_12.csv，数据形状: (1634, 2)\n",
      "所有文件合并完成，总数据形状: (10004, 2)\n",
      "帧率抖动: 0.01 ms\n",
      "丢包率: 0.0\n",
      "真实采样率: 100.0\n",
      "1752724388538.0\n",
      "对齐后脑eeg数据：(743000, 19)\n",
      "对齐后脑ppg数据：(148600, 5)\n",
      "对齐后脑电采集时间：1486.0\n",
      "对齐后PPG的采集时间：1486.0\n",
      "ppg开始时间1752724400085.0ms, imu开始时间1752724400080.0ms\n",
      "ppg开始时间1752724539675.0ms, imu开始时间1752724539670.0ms\n",
      "ppg开始时间1752724734325.0ms, imu开始时间1752724734320.0ms\n",
      "ppg开始时间1752724769595.0ms, imu开始时间1752724769590.0ms\n",
      "ppg开始时间1752724804291.0ms, imu开始时间1752724804289.0ms\n",
      "ppg开始时间1752724843871.0ms, imu开始时间1752724843869.0ms\n",
      "ppg开始时间1752724885221.0ms, imu开始时间1752724885219.0ms\n",
      "ppg开始时间1752725078271.0ms, imu开始时间1752725078269.0ms\n",
      "ppg开始时间1752725209861.0ms, imu开始时间1752725209859.0ms\n",
      "ppg开始时间1752725414031.0ms, imu开始时间1752725414029.0ms\n",
      "ppg开始时间1752725545061.0ms, imu开始时间1752725545059.0ms\n",
      "ppg开始时间1752725737591.0ms, imu开始时间1752725737589.0ms\n",
      "\n",
      "事件1：\n",
      "  触发器: 3->30\n",
      "  时间范围: 1752724400080.0ms - 1752724520082.0ms\n",
      "  提取到EEG样本数: 60002\n",
      "  提取到PPG样本数: 12000\n",
      "  提取到IMU样本数: 12000\n",
      "\n",
      "事件2：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752724539670.0ms - 1752724719722.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件3：\n",
      "  触发器: 25->20\n",
      "  时间范围: 1752724734320.0ms - 1752724759646.0ms\n",
      "  提取到EEG样本数: 12664\n",
      "  提取到PPG样本数: 2533\n",
      "  提取到IMU样本数: 2533\n",
      "\n",
      "事件4：\n",
      "  触发器: 27->20\n",
      "  时间范围: 1752724769588.0ms - 1752724795298.0ms\n",
      "  提取到EEG样本数: 12856\n",
      "  提取到PPG样本数: 2571\n",
      "  提取到IMU样本数: 2571\n",
      "\n",
      "事件5：\n",
      "  触发器: 28->20\n",
      "  时间范围: 1752724804286.0ms - 1752724834772.0ms\n",
      "  提取到EEG样本数: 15244\n",
      "  提取到PPG样本数: 3049\n",
      "  提取到IMU样本数: 3049\n",
      "\n",
      "事件6：\n",
      "  触发器: 26->20\n",
      "  时间范围: 1752724843870.0ms - 1752724870612.0ms\n",
      "  提取到EEG样本数: 13372\n",
      "  提取到PPG样本数: 2675\n",
      "  提取到IMU样本数: 2675\n",
      "\n",
      "事件7：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752724885220.0ms - 1752725065272.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18006\n",
      "  提取到IMU样本数: 18006\n",
      "\n",
      "事件8：\n",
      "  触发器: 1->10\n",
      "  时间范围: 1752725078270.0ms - 1752725198322.0ms\n",
      "  提取到EEG样本数: 60027\n",
      "  提取到PPG样本数: 12006\n",
      "  提取到IMU样本数: 12006\n",
      "\n",
      "事件9：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752725209854.0ms - 1752725389906.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件10：\n",
      "  触发器: 4->40\n",
      "  时间范围: 1752725414030.0ms - 1752725534090.0ms\n",
      "  提取到EEG样本数: 60031\n",
      "  提取到PPG样本数: 12006\n",
      "  提取到IMU样本数: 12006\n",
      "\n",
      "事件11：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752725545054.0ms - 1752725725106.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件12：\n",
      "  触发器: 5->50\n",
      "  时间范围: 1752725737582.0ms - 1752725857640.0ms\n",
      "  提取到EEG样本数: 60030\n",
      "  提取到PPG样本数: 12005\n",
      "  提取到IMU样本数: 12005\n"
     ]
    }
   ],
   "source": [
    "# 读取前测eeg ppg imu\n",
    "precedent_eeg_path=r\"D:\\multimode_database\\sub13-songweifeng\\gateway_eegdata_csv\\songweifeng_1752718597_1752720246_precedent_stage_signals_1.csv\"\n",
    "ppg_folder=r\"D:\\multimode_database\\sub13-songweifeng\\watch\\ppg_green_raw\\250717\"\n",
    "imu_folder=r\"D:\\multimode_database\\sub13-songweifeng\\watch\\imu_raw\\250717\"\n",
    "hr_folder=r\"D:\\multimode_database\\sub13-songweifeng\\watch\\ppg_hr\\250717\"\n",
    "hrv_folder=r\"D:\\multimode_database\\sub13-songweifeng\\watch\\ppg_hrv\\250717\"\n",
    "pre_event_signals=Data_Align.align_Epoch_data(precedent_eeg_path, ppg_folder, imu_folder, hr_folder, hrv_folder)\n",
    "# 读取后测eeg\n",
    "post_eeg_path=r\"D:\\multimode_database\\sub13-songweifeng\\gateway_eegdata_csv\\songweifeng_1752724386_1752725875_posterior_stage_signals_1.csv\"\n",
    "post_event_signals=Data_Align.align_Epoch_data(post_eeg_path, ppg_folder, imu_folder, hr_folder, hrv_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "6862c191",
   "metadata": {},
   "outputs": [],
   "source": [
    "# class ssa_process:    \n",
    "#     def __init__(self, n_freq_bins=4096):\n",
    "#         self.n_freq_bins = n_freq_bins\n",
    "        \n",
    "#     def ssa(self,ts: np.ndarray, L: int, perform_grouping: bool = True, wcorr_threshold: float = 0.3, ret_Wcorr: bool = False):\n",
    "#             \"\"\"\n",
    "#             Performs SSA on ts\n",
    "#             https://www.kaggle.com/jdarcy/introducing-ssa-for-time-series-decomposition\n",
    "\n",
    "#             Parameters\n",
    "#             ----------\n",
    "#                 ts : ndarray of shape (n_timestamps, )\n",
    "#                     The time series to decompose\n",
    "#                 L : int\n",
    "#                     first dimension of the L-trajectory-matrix\n",
    "#                 grouping : bool, default=True\n",
    "#                     If True, perform grouping based on the w-correlations of the deconstructed time series\n",
    "#                     using agglomerative hierarchical clustering with single linkage.\n",
    "#                     If this parameter is True, the parameter distance_threshold must be set.\n",
    "#                 wcorr_threshold : float, default=0.3\n",
    "#                     The w-correlation threshold used with the agglomerative hierarchical clustering.\n",
    "#                     Time series with at least this w-correlation will be grouped together.\n",
    "#                     This parameter will be ignored if grouping is set to False.\n",
    "#                 ret_Wcorr : bool, default=False\n",
    "#                     Whether the resulting w-correlation matrix should be returned.\n",
    "#                     If grouping is enabled, return the w-correlation matrix of the grouped time series.\n",
    "#                     If grouping is disabled, return the w-correlation matrix of the ungrouped time series.\n",
    "                \n",
    "\n",
    "#             Returns\n",
    "#             ----------\n",
    "#                 Y : ndarray of shape (n_groups, n_timestamps) if grouping is enabled and (L, n_timestamps) if it is disabled.\n",
    "#                 Wcorr : ndarray\n",
    "#                     The Wcorrelation matrix.\n",
    "#                     Wcorr will only be returned if ret_Wcorr is True\n",
    "#             \"\"\"\n",
    "#             N = len(ts)\n",
    "#             K = N - L + 1\n",
    "\n",
    "#             L_trajectory_matrix = hankel(ts[:L], ts[L-1:]) # (L, K)\n",
    "#             U, Sigma, V = np.linalg.svd(L_trajectory_matrix) # (L, L); (d, ); (K, K)\n",
    "#             V = V.T # (K, K)\n",
    "#             d = len(Sigma)\n",
    "\n",
    "#             deconstructed_ts = []\n",
    "#             for i in range(d):\n",
    "#                 X_elem = np.array(Sigma[i] * np.outer(U[:,i], V[:,i])) # (L, K)\n",
    "#                 X_elem_rev = X_elem[::-1] # (L, K)\n",
    "#                 ts_i = np.array([X_elem_rev.diagonal(i).mean() for i in range(-L+1, K)])\n",
    "#                 deconstructed_ts.append(ts_i)\n",
    "#             deconstructed_ts = np.array(deconstructed_ts) # (d, L, K)\n",
    "            \n",
    "#             if not perform_grouping and not ret_Wcorr:\n",
    "#                 return deconstructed_ts\n",
    "            \n",
    "\n",
    "#             w = np.concatenate((np.arange(1, L+1), np.full((K-L,), L), np.arange(L-1, 0, -1)))\n",
    "#             def wcorr(ts1: np.ndarray, ts2: np.ndarray) -> float:\n",
    "#                 \"\"\"\n",
    "#                 weighted correlation of ts1 and ts2.\n",
    "#                 w is precomputed for reuse.\n",
    "#                 \"\"\"\n",
    "#                 w_covar = (w * ts1 * ts2).sum()\n",
    "#                 ts1_w_norm = np.sqrt((w * ts1 * ts1).sum())\n",
    "#                 ts2_w_norm = np.sqrt((w * ts2 * ts2).sum())\n",
    "                \n",
    "#                 return w_covar / (ts1_w_norm * ts2_w_norm)\n",
    "\n",
    "#             Wcorr_mat = pairwise_distances(deconstructed_ts, metric=wcorr)\n",
    "\n",
    "#             if not perform_grouping:\n",
    "#                 return deconstructed_ts, Wcorr_mat\n",
    "\n",
    "#             Wcorr_mat_dist = 1 - Wcorr_mat\n",
    "#             distance_threshold = 1 - wcorr_threshold\n",
    "#             agg_clust = AgglomerativeClustering(metric='precomputed', linkage='single',\n",
    "#                                                 distance_threshold=distance_threshold, n_clusters=None)\n",
    "#             clust_labels = agg_clust.fit_predict(Wcorr_mat_dist)\n",
    "#             n_clusters = clust_labels.max() + 1\n",
    "#             grouped_ts = [np.sum(deconstructed_ts[clust_labels == cluster_id], axis=0) \n",
    "#                         for cluster_id in range(n_clusters)]\n",
    "#             grouped_ts = np.array(grouped_ts)\n",
    "            \n",
    "#             if not ret_Wcorr:\n",
    "#                 return grouped_ts\n",
    "            \n",
    "#             Wcorr_mat = pairwise_distances(grouped_ts, metric=wcorr)\n",
    "#             return grouped_ts, Wcorr_mat\n",
    "    \n",
    "#     def _get_dominant_frequencies(self, spectrum: np.ndarray, axis=-1, threshold=.5):\n",
    "#         \"\"\"\n",
    "#         Given the frequency spectra of one or multiple signals, compute the dominant frequencies along a specified axis.\n",
    "\n",
    "#         Parameters\n",
    "#         ----------\n",
    "#             sig : ndarray\n",
    "#                 The signals to compute the dominant frequencies on.\n",
    "#             axis : int, default=-1\n",
    "#                 The axis along which to compute the dominant frequencies.\n",
    "#             threshold : float, default=0.5\n",
    "#                 The threshold which divides dominant and non-dominant frequencies.\n",
    "#                 A dominant frequency has a peak of amplitude of higher than threshold times the maximum amplitude in that spectrum\n",
    "\n",
    "#         Returns\n",
    "#         ----------\n",
    "#             dom_freqs : ndarray\n",
    "#                 dom_freqs has the same shape as sig.\n",
    "#                 Iff a value in sig corresponds to a dominant frequency, this value is set to True in dom_freqs.\n",
    "#         \"\"\"\n",
    "\n",
    "#         max_amplitudes = np.max(spectrum, axis=axis, keepdims=True)\n",
    "#         dom_freqs = spectrum > threshold * max_amplitudes\n",
    "\n",
    "#         return dom_freqs\n",
    "\n",
    "#     def get_Facc(self, acc_window: np.ndarray, hr: int, delta: int = 10):\n",
    "#         # from actual hr ger hr_idx\n",
    "#         hr_hz = hr / 60\n",
    "#         hr_idx = int(round(hr_hz/100*self.n_freq_bins))\n",
    "\n",
    "#         _, acc_freqs = signal.periodogram(acc_window, nfft=self.n_freq_bins * 2 - 1)\n",
    "#         acc_dom_freqs = self._get_dominant_frequencies(acc_freqs)\n",
    "#         F_acc = np.logical_or.reduce(acc_dom_freqs)\n",
    "#         N_p = np.arange(hr_idx, self.n_freq_bins + 1, hr_idx)\n",
    "#         for idx in N_p:\n",
    "#             F_acc[idx-delta : idx+delta] = False\n",
    "        \n",
    "#         return F_acc       \n",
    "\n",
    "#     def filter_ssa_groups(self, ssa_groups: np.ndarray, F_acc: np.ndarray):\n",
    "#         _,ssa_groups_spectra = signal.periodogram(ssa_groups, nfft=self.n_freq_bins * 2 - 1)\n",
    "#         ssa_dom_freqs = self._get_dominant_frequencies(ssa_groups_spectra)\n",
    "#         group_filter = np.logical_or.reduce(np.logical_and(ssa_dom_freqs, F_acc), axis=1)\n",
    "#         group_filter = np.logical_not(group_filter)\n",
    "\n",
    "#         return ssa_groups[group_filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "29315bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# event = 0\n",
    "# raw_ppg_signal = pre_event_signals[event]['ppg_data'][:, 1:5]  # shape: (n_samples, 4)  # shape: (n_samples,)\n",
    "# # windowring\n",
    "\n",
    "# window_length = 10\n",
    "# i=5\n",
    "# raw_ppg_signal = raw_ppg_signal[window_length*100*(i-1):window_length*100*i]\n",
    "# normalized_ppg = (raw_ppg_signal - np.mean(raw_ppg_signal, axis=0)) / np.std(raw_ppg_signal, axis=0)\n",
    "# raw_ppg_signal = np.mean(normalized_ppg, axis=1)\n",
    "# IMU_signal = np.array(pre_event_signals[event]['imu_data'][window_length*100*(i-1):window_length*100*i, 1: 4])\n",
    "# t=np.array(pre_event_signals[event]['ppg_data'][window_length*100*(i-1):window_length*100*i,0])\n",
    "# hr_mask=(pre_event_signals[event]['hr_data'][:,0] >= t[0]) & (pre_event_signals[event]['hr_data'][:,0]<=t[-1])\n",
    "# hrv_mask=(pre_event_signals[event]['hrv_data'][:,0] >= t[0]) & (pre_event_signals[event]['hrv_data'][:,0]<=t[-1])\n",
    "# hr=np.array(pre_event_signals[event]['hr_data'][hr_mask, 1])\n",
    "# hrv_window=np.array(pre_event_signals[event]['hrv_data'][hrv_mask, :])\n",
    "# hr=np.mean(hr)\n",
    "# # 0.3-5hz 4 order butterworth bandpass filter in band ma removal\n",
    "# b, a = signal.butter(4, [0.4, 5], btype='bandpass', fs=100)\n",
    "# filtered_signal = signal.filtfilt(b, a, raw_ppg_signal)\n",
    "# # IMU filtering\n",
    "# filtered_IMU_x = signal.filtfilt(b, a, IMU_signal[:, 0])\n",
    "# filtered_IMU_y = signal.filtfilt(b, a, IMU_signal[:, 1])\n",
    "# filtered_IMU_z = signal.filtfilt(b, a, IMU_signal[:, 2])\n",
    "# filtered_IMU = np.column_stack((filtered_IMU_x, filtered_IMU_y, filtered_IMU_z))\n",
    "# # SSA on the ppg signal, attain groped ppg\n",
    "# ssa_class = ssa_process(n_freq_bins=4096)\n",
    "# grouped_ppg= ssa_class.ssa(filtered_signal, int(0.3*len(filtered_signal)), True, 0.3, False) # n*1000\n",
    "# # Get F_acc heart_rate=69\n",
    "# input_IMU_signal = np.copy(filtered_IMU)\n",
    "# input_IMU_signal = np.transpose(input_IMU_signal) # 3*1000\n",
    "# F_acc = ssa_class.get_Facc(input_IMU_signal, hr, 10)\n",
    "# # SSA_group filtering\n",
    "# filtered_grouped_ppg = ssa_class.filter_ssa_groups(grouped_ppg, F_acc)\n",
    "# filtered_ppg = np.sum(filtered_grouped_ppg, axis=0)\n",
    "# hrv_window[:,1]=max(filtered_ppg)\n",
    "# # # adaptive filtering\n",
    "# # adaptive_filter = pa.filters.FilterRLS(n=3, mu=0.99, eps=0.1)\n",
    "# # y,e,w = adaptive_filter.run(filtered_signal, filtered_IMU)\n",
    "# # SSA performed on the PPG signal\n",
    "# import neurokit2 as nk\n",
    "# filtered_signal = nk.ppg_clean(filtered_signal, sampling_rate=100)\n",
    "# peaks, info = nk.ppg_peaks(filtered_signal, sampling_rate=100,method='elgendi',correct_artifacts=True)\n",
    "# filtered_ppg = nk.ppg_clean(filtered_ppg, sampling_rate=100)\n",
    "# peaks1, info1 = nk.ppg_peaks(filtered_ppg, sampling_rate=100, method='bishop',correct_artifacts=False)\n",
    "# hrv=nk.hrv_time(peaks, sampling_rate=100)\n",
    "# # nk.ppg_plot(signals, info)\n",
    "# pks_idx=info[\"PPG_Peaks\"]\n",
    "# pks_y=np.ones(len(pks_idx))*max(filtered_signal)\n",
    "# pks_idx1=info1[\"PPG_Peaks\"]\n",
    "# pks_y1=np.ones(len(pks_idx1))*max(filtered_ppg)\n",
    "# plt.figure(figsize=(18, 6))\n",
    "# plt.plot(t,raw_ppg_signal)\n",
    "# plt.figure(figsize=(18, 6))\n",
    "# plt.plot(t,filtered_signal)\n",
    "# plt.scatter(t[pks_idx], pks_y, color='red')\n",
    "# # plt.figure(figsize=(18, 6))\n",
    "# # plt.plot(t, filtered_ppg)\n",
    "# # plt.scatter(t[pks_idx1], pks_y1, color='red')\n",
    "# # plt.scatter(hrv_window[:,0], hrv_window[:,1], color='red')\n",
    "# # plt.scatter(t[pks_idx], pks_y, color='green')\n",
    "# # plt.plot(t, filtered_IMU)\n",
    "# # plt.figure(figsize=(18, 6))\n",
    "# # plt.plot(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "cbb8467a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def poincare_features(rr_intervals):\n",
    "    rr_intervals = np.asarray(rr_intervals)\n",
    "    rr_n = rr_intervals[:-1]\n",
    "    rr_n1 = rr_intervals[1:]\n",
    "    \n",
    "    diff_rr = rr_n1 - rr_n\n",
    "\n",
    "    sd1 = np.sqrt(np.var(diff_rr) / 2)\n",
    "    sd2 = np.sqrt(2 * np.var(rr_intervals) - sd1**2)\n",
    "\n",
    "    return sd1, sd2, sd2/sd1\n",
    "\n",
    "def window_feature_extraction(window_ppg, window_t, event_hr, event_hrv, event_trigger, fs=100):\n",
    "    # normalized_ppg=(window_ppg-np.mean(window_ppg, axis=0))/np.std(window_ppg, axis=0)\n",
    "    # window_norm_ppg=np.mean(normalized_ppg, axis=1)\n",
    "    # window_norm_ppg=np.mean(window_ppg, axis=1)\n",
    "    # hrv & hr\n",
    "    hr_mask=(event_hr[:,0]>=window_t[0])&(event_hr[:,0]<=window_t[-1])\n",
    "    hrv_mask=(event_hrv[:,0]>=window_t[0])&(event_hrv[:,0]<=window_t[-1])\n",
    "    window_hr=np.mean(event_hr[hr_mask, 1])\n",
    "    window_hrv=event_hrv[hrv_mask, :]\n",
    "    # ppg_filtering\n",
    "    b, a=signal.butter(4, [0.5, 5], btype='bandpass', fs=fs)\n",
    "    window_filtered_ppg=[]\n",
    "    for i in range(4):\n",
    "        filt_signal=signal.filtfilt(b, a, window_ppg[:, i])\n",
    "        window_filtered_ppg.append(filt_signal.reshape(-1, 1))\n",
    "    window_filtered_ppg=np.concatenate(window_filtered_ppg, axis=1)\n",
    "    normalized_ppg=(window_filtered_ppg-np.mean(window_filtered_ppg, axis=0))/np.std(window_filtered_ppg, axis=0)\n",
    "    window_norm_ppg=np.mean(normalized_ppg, axis=1)\n",
    "    window_filtered_ppg=nk.ppg_clean(window_norm_ppg, sampling_rate=fs)\n",
    "    window_peaks, window_info=nk.ppg_peaks(window_filtered_ppg, sampling_rate=fs, method='elgendi', correct_artifacts=True)\n",
    "    pks=np.array(window_peaks.PPG_Peaks)\n",
    "    window_peaks_idx=np.where(pks!=0)[0]\n",
    "    # calculate hrv for the right beats\n",
    "    if np.all((np.diff(window_peaks_idx/fs)<=2) & (np.diff(window_peaks_idx/fs)>=0.3)) & (len(window_peaks_idx)>4):\n",
    "        if event_trigger != 6:\n",
    "            window_y=1\n",
    "        else:\n",
    "            window_y=0\n",
    "        # time_domain hrv\n",
    "        window_t_hrv=nk.hrv_time(window_peaks, sampling_rate=fs)\n",
    "        # frequency_domain hrv\n",
    "        window_f_hrv_l=nk.hrv_frequency(window_peaks, sampling_rate=fs, psd_method='lomb')\n",
    "        window_f_hrv_w=nk.hrv_frequency(window_peaks, sampling_rate=fs, psd_method='welch')\n",
    "        window_f_hrv_m=nk.hrv_frequency(window_peaks, sampling_rate=fs, psd_method='multitapers')\n",
    "        # print(\"Lomb HRV_HF is NaN:\", pd.isnull(window_f_hrv_l[\"HRV_HF\"]).item())\n",
    "        # print(\"Welch HRV_HF is NaN:\", pd.isnull(window_f_hrv_w[\"HRV_HF\"]).item())\n",
    "        # print(\"Multitaper HRV_HF is NaN:\", pd.isnull(window_f_hrv_m[\"HRV_HF\"]).item())\n",
    "        # non_linear hrv\n",
    "        # Poincare_plot\n",
    "        sd1, sd2, sd2_sd1 = poincare_features(1000*np.diff(window_peaks_idx/fs))\n",
    "        # Sampen1 Sampen2\n",
    "        Sampen1 = nolds.sampen(window_filtered_ppg, emb_dim=1, tolerance=0.2*np.std(window_filtered_ppg))\n",
    "        Sampen2 = nolds.sampen(window_filtered_ppg, emb_dim=2, tolerance=0.2*np.std(window_filtered_ppg))\n",
    "        # # DFA1 DFA2\n",
    "        dfa1=nolds.dfa(window_filtered_ppg, order=1)\n",
    "        dfa2=nolds.dfa(window_filtered_ppg, order=2)\n",
    "        # # # shannon_entropy\n",
    "        # shannon_en=scipy.stats.entropy(window_filtered_ppg)\n",
    "        # # # fuzzy entropy\n",
    "        # fuzz_en=EntropyHub.FuzzEn(window_filtered_ppg)\n",
    "        window_data=[]\n",
    "        # label\n",
    "        window_data.extend([\n",
    "        event_trigger,\n",
    "        window_y,\n",
    "        # Time-domain\n",
    "        window_hr,\n",
    "        window_t_hrv['HRV_MeanNN'].item(),\n",
    "        window_t_hrv['HRV_SDNN'].item(),\n",
    "        window_t_hrv['HRV_RMSSD'].item(),\n",
    "        window_t_hrv['HRV_SDSD'].item(),\n",
    "        window_t_hrv['HRV_pNN20'].item(),\n",
    "        window_t_hrv['HRV_pNN50'].item(),\n",
    "        # Frequency-domain lomb\n",
    "        window_f_hrv_l['HRV_HF'].item(),\n",
    "        window_f_hrv_l['HRV_TP'].item(),\n",
    "        window_f_hrv_l['HRV_HFn'].item(),\n",
    "        window_f_hrv_l['HRV_LnHF'].item(),\n",
    "        # Frequency-domain welch\n",
    "        window_f_hrv_w['HRV_HF'].item(),\n",
    "        window_f_hrv_w['HRV_TP'].item(),\n",
    "        window_f_hrv_w['HRV_HFn'].item(),\n",
    "        window_f_hrv_w['HRV_LnHF'].item(),\n",
    "        # Frequency-domain milti\n",
    "        window_f_hrv_m['HRV_HF'].item(),\n",
    "        window_f_hrv_m['HRV_TP'].item(),\n",
    "        window_f_hrv_m['HRV_HFn'].item(),\n",
    "        window_f_hrv_m['HRV_LnHF'].item(),\n",
    "        # non-linear\n",
    "        sd1,\n",
    "        sd2, sd2_sd1,\n",
    "        Sampen1,\n",
    "        Sampen2,\n",
    "        # # shannon_en,\n",
    "        # # fuzz_en,\n",
    "        dfa1,\n",
    "        dfa2\n",
    "        ])\n",
    "        window_data=np.array(window_data).reshape(1,-1)\n",
    "        return window_data\n",
    "\n",
    "\n",
    "from joblib import Parallel, delayed\n",
    "import numpy as np\n",
    "\n",
    "def process_event(event, window_length, step, fs=100):\n",
    "    event_ppg = event['ppg_data'][:, 1:5]\n",
    "    event_t = event['ppg_data'][:, 0]\n",
    "    event_hrv = event['hrv_data']\n",
    "    event_hr = event['hr_data']\n",
    "    event_trigger = event['start_trigger']\n",
    "    \n",
    "    window_length_p = window_length * fs\n",
    "    step_p = step * fs\n",
    "    features = []\n",
    "\n",
    "    if len(event_t) < window_length_p:\n",
    "        window_features = window_feature_extraction(event_ppg, event_t, event_hr, event_hrv, event_trigger, fs)\n",
    "        if window_features is not None:\n",
    "            features.append(window_features)\n",
    "    else:\n",
    "        for start in range(0, len(event_t) - window_length_p + 1, step_p):\n",
    "            end = start + window_length_p\n",
    "            window_ppg = event_ppg[start:end]\n",
    "            window_t = event_t[start:end]\n",
    "            window_features = window_feature_extraction(window_ppg, window_t, event_hr, event_hrv, event_trigger, fs)\n",
    "            if window_features is not None:\n",
    "                features.append(window_features)\n",
    "\n",
    "    return features\n",
    "\n",
    "def set_construction(event_signal, window_length, step, fs=100, n_jobs=-1):\n",
    "    all_features = Parallel(n_jobs=n_jobs)(\n",
    "        delayed(process_event)(event, window_length, step, fs)\n",
    "        for event in event_signal\n",
    "    )\n",
    "    \n",
    "    # Flatten and concatenate\n",
    "    flat_data = [f for features in all_features for f in features if features is not None]\n",
    "    dataset = np.concatenate(flat_data, axis=0)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "2c2454aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=set_construction(pre_event_signals, 30, 1, fs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "5432adaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=set_construction(post_event_signals, 30, 1, fs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "4ef3a7b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8292682926829268\n",
      "[[154  37]\n",
      " [ 26 152]]\n",
      "0.7907608695652174\n",
      "[[150  40]\n",
      " [ 37 141]]\n",
      "0.8369565217391305\n",
      "[[170  20]\n",
      " [ 40 138]]\n",
      "0.8179347826086957\n",
      "[[166  24]\n",
      " [ 43 135]]\n",
      "0.8555858310626703\n",
      "[[164  26]\n",
      " [ 27 150]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(np.float64(0.0), np.float64(1.0), np.float64(0.0), np.float64(1.0))"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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9xzJ46i75e2/0Pxvm1H1VeD2TuENSzzd5Yvmia1lpWeO1aI+7+/CJfP7DLlm/57LVbSfy9pIB7UrIe40LSaJE3lEeF/rkuYz6db8sWHda4oszS9o7/RorLv7p8Dab5qzv8DbhfuI6zgqTk+KJ9u87JoMHT5ILF67GeKxKfn35VS+pWLFEvErqBQeHyvDh02X5so3RHheQKoWMGNFb6tR5w+q21YCy8d/Nll9+WaElR6OSPHlS+eyz96R1G8/9+yp3iWUS32OsBTPaSvnS2bV9W5N62/7oIdmzpnJaUq9ZgyLy1aA6kjJF8miPm7PogIwYt1GePHkuzuDrk0S+GlRX3moS/e8Ld++Fymdf/il/bTpldduJEnnJx32qSbcO5aKPsUKfyYhxG2Te4oMSn1w4+JnTr0GcBQAAAE8W9W8Jdjh58qQULFhQhgwZIrt375ZTp4y/vPTr108bPa7yiOaPkJAQ6dWrl8yYMcORt5Og/DTlL0NCz1pHD1+Sbu0mxpjQU+7dDZZBH82Sad+vEWe4FXhfenX+IcaEnhJ445707Tpdtvz9n1VthwQ/ln7df4wxoac8uB8ig/vPlkXztlnVNlxPJapMCT1bFc+XVpaOeTPGhJ6SNiC5TPusugxsZ7nj1pKCOd1jBoJf8sQya2itGBN6SuoUyWTqp9WkU8MCVif0Jn9cVd5vViTaziZFzdwb3r28DO5c2up7TygjyB39gOcjznKOJUv+lo4dP7cqoadcvnxDunUdJgvmOyceMpcrZxaHtKNmHHbpMjTGhJ5y/95D6dN7tMyetdLqhN6HfcfIzJnLok3oKY8fP5Vhw6bK6FE/WX3vcK2vPqujJ/RslcI/md0JPWu836mcTBjVOMaEntLhnVKy4Mc2WnUCR/PzTSpzp7WOMaGnpE7lI9PHt5B325axOqE39dvm0qNzhZhjLJ8kMvJ/b8qQgbWsvveEgjgLAAAAnsxh5TfVL/HNmzeX69ev68+dPXtW37548aL8/PPP4uX1KiJW5aDq1asnBw8elC1btmidTp988ok0bdpUMmTI4KjbShAO/3tefpu9xa5ZcZ/0mSkhweElYYqWyCntu9SUoiVyaeUtr1+9I3+vPSgLft0sjx+/Ks839+dNkjNPBqnf2LpfTq3tEBry8Vy5cim8VE6Bwtmk8/u1pFjJ3JI0aRI5e/qa/D5vu1YiVHnx4qWM+N9vki1nOnktX+Zo2x/75WItgWmSLUc66dKjjpSpkE/8/JLLpQuBsmLJblnx+y7tu6hMGbdacufJKGXfsK+EEJzj886lpV09+/5MVJJuxuc1xN+sjOz+E4EybelROXAySEIfP5dsGf2lUaVc0rVpYW2mm9Kjxety5sp9Wb7lfLTtZ07nK6lSJNP3P5m8U5ZuOidxYWTPClpZUZML1x9oMxp3HL4hj0KfSZ4sKaV1nbzSpm5+bVS+MqhTaTlz+b52THT6ty0hdcqFd/gF3Q3VZkpu2ndV7jx8LNky+Euzarmla5PCemfau40LaWVNl8TR5wHEd8RZzrFr12H5YugULaYwKV++qHTo2FhKlCggAQEp5MGDR3Lw3xMyZ85q7XhFHT98+I+SNWtGqVrNOGhhzpxRdt3LmDEz5ddfVuj7rVu/KY2bVBdHGDpkilZS1CRnzszSu3cbeaNicfH399XKZi5a+Jf89ttaPQ4aO/ZneS1vdqlUqWS0bU8YP0c2bNit76tym6rt6jXKSurUKeXqlUBZsXKT/DxzmTx79mrW1KxZKyV/gVzSsmVth7w/OMb/BtSU9u+Usvv8QvmNf7e07jpfdu0Lj79jo3rlPPLZhzX0/SdPn8uM2Xtk2R9H5crVe5IqlY/UrppXPuxeWTKkf1U+tlTxrDJ66JsyYMgf4khjhr6ptW1y/tIdbTbjjl0X5GHwU3ktVxpp07KEtHurpB5jDe5fU06fuyXbd12Itu2Pe1czlC8NuvVIa3vD1rNy916IZMsSIM0bvS7dOpbTSn0q77UvKydOB8rvK6wb7AgAAAAgfnPYTL1FixbJiRMntM6klClTaqPBe/bsqb++YMECffRu/vz5Zc+ePTJ+/HjZtGmTfPbZqxIcau2XuXPnOuqWEoTQkKcycshCefnS9iqqk75dJffvhej7DZuVlck/95JK1QprJSiTJk0sOXNnkPd61pVJM3uKj9kadt+NWq7N3HOUFYt3yX8Hw3/JrVKjiEyf3Vsr9RmQyk9by+/14rnky6/bS7/PmunHhYY+lYlfRz+SfOfW43oiUClcNIf8vPAjqduwlKRJm0KSJU8i+QpmlYGDW8jI7zrqo2JVh913o5fraxQibvn7JNFmh3VpbN96daaEYJqU4aO7f99wRtoMWS+b9l+V+4+eytPnL+Xc1Qdagqrd0PUSHBq+ztywbuW0GW3RKZzLWO72+Pm7Eheql8oqjSrn0vcPngqSJgPXyMptF7SSok+evpDjF+7KFzP2Sq9vtsjz/+/MTpzIW77oWk6biReV/DlSSdcm4X8Gl248lMYD/9DKa964EyJPn736DL+bf0jaDjF+hipp6O+bxGnvOz5hBDlsRZzleOrf9xHDpxv+ne/Xv4PMmj1SateuIOnSpZYkSRJL2rSppFbtCvLrrBHSt294KUz1eX/55VRtTbrYWvvndkNCT63f978hjilLv3nzXlmzJrz6QPHi+WXpsglawlC9x+TJk0mhQnnki2E9ZdLkQYY4aPhXP0YbB508eUF+/nm5oTTpsuXfa+U1M2VKJ8mSJZU8r2WTfv06yJy5o8TXN/zf4K/H/iwPH7IemDvw90uqzQ7r2qFcrNopXMCY1Dt28qY4QrKkiWTk4Hp6gkwNNOzQY6F8O3mrnD1/W4trbgY+0spQNmj1s5w6G6Sf27JxUSlbMps4So0qr0njNwvr+/8evioNW/0iK9Yck1t3QrRyn8dOBsqQUeukx4Cl8vz5/8dYib3ly8/qaDPxolIgb3otWWdy8fJdadDqF+193Qh8qL3PsxfuaO+79XvzJTjkqX6sShqqmZJ4hTgLAAAAnsxhST21tovJypUr5b333pOAgAD9ueXLw3/hV51Q/v6vRlAqw4cP10eNr1q1ylG3lCBMGb9arl6+bfN5N2/ck41mia4cudLLx/9rGWWZl0KvZ5duvd/U90NDnsiaFXvFEZ4/eyGzZ2zQ9zNkDJCho9pK4ijK5bRsU0matwpf52X/njOyb3fU63XNnLpO31aJyZHjOoqvr+VfeqvWfF269w1fL+HyxSBZa0XJTjhXoVypZfk39eXNCjnsbiNzWl9pWCmnvn/26n0ZMn13lAnx/87elvELDhmSii1rRl/KsmCu8NKbT5+90Gb3xYUPWxXTt1VS7YNvtkrIY8tryvy954p8O++gYb3BFjWiXquy7ztFDR2+vb/dKrfuPbZ47MHTt+SzKf/o+2oWo5q9BxHVp+foBzwbcZbjbd2yT86cCV9LtFGjatK9+9vRntPrg9bSoGEVff/q1UAtIRcbN27ckqFDp+j7aubcuG8HSOLEjikbOGniAn1bJdUmThokfn4+Fo9Vycz+/Tvq+6ok6fJl4TFaRFOm/KYnk729veX7iZ9K+vSWy1CXKFFQRo3qq+/fv/9Im72HuKUScasWdJb6ta0rwW3tTL0r1+7Lg4fh1UBio1Xz4pI1c/jfd2MnbpE9ByyvA6wSa136LDYkvNT6dI7Sr0dlfVtdo8fAZRJiNoDJ3LpNp+XrSZv1/ddypdWSjFH5sHslQ4zVa+ByCbptOfGt1h78+IvwGYipAnwMCcGEjjgLAAAAnsxhST21tosaPV6qVCmpWrWq4bXbt2/L3r3hCSBV+slcokSJpGLFilq5nzNnzjjqljzenp0nZfmiVx3mauRqORvKRKqEnnmpqTadqkWZRDN5s0lpfYSssm9X1Ik0W+zYckxu33qo77/Tvoo2My863T54U5thZ7Jq6R6Lx508fsWwXqCajZg+Y3ingCXq+ukzhB+zall4SSm4lpoZp9bPWzq2vuTKnFJ/Xs02s1WDijm1mWgmM1ccl+cvop/hunTzOcPPSaVimaI9vlDu8I7Mc9ceyLP/H53tSkVypzGsF7h441m5eSc02nN+WXVcbph1Gr1TK2+U5UtrlQ0vu7n5wDU5FsNsxD//uSQHT4WX1X0rhsQoAMuIsxzvr792GvZ7fdDKqvN69jQet3nLvljdx+efT5QHD8L/Dh406D3Jlj36f2+sdfToWTl6NPzPvEXL2pIxY/Rrynbq3NRwzO+L11s87tatu7LRrOxmtWplpHDh6P+Of7N+ZSlWLL9hPUNTuU+4llrrTc0cWzG3k+TOEV5p4NYd+2dPFi4Qvt7x8VOB4iiqlKXJ7TshMu/3f6M9XiUU5yw8oO+XK5Vd8uQ0VlOwR5GCGaVYkfCS/4uWH9ZmCEZn5ty9cv3mA32/dfPiFo9Ll8ZX6lTPp+9v3HZWjsYw03HN+pNy8L9r+v47zcIHdQEAAADwXA5L6gUFBeklnyJav3699gu76ozKmzev5MwZPlvGJG3atIZ2EL2HD0JlzLDf9f3WHatKkeKRP9eoHDtiXN+icvUiMZ6TMqWvpErtp+9fu3pHHGHb5qOG/Vr1SsR8LwG+hiSmKrH55EnkUbLbN0Vo+82Y21Yj42vUDf+l+Nh/l7WZjXAtlUDbMKWpdKhfQJIkDv+raufh69Jl+Eab2yueP51h/++9lkd4m3sQ/FTuPAgfZZ49Y/jMF0sK5QxP6p24EDelN2uXM5aYWr0j+rVblBcvw+TPneF/JxTPl06b2RhR9VJZDH8Wq7fH3Layanv4WoSZ0vpK6YLha/0lVN5eYQ5/wLMRZzneoUOn9O2sWTNInjzWlejLly+HNpvO5MKF8E51W61csUl27jhoWM+v5Vt1xFHM17pTGjQIn2UYXRz05puV9P3Dh07J9euRvzdbtuw3lOY0n8EYnUaNwpPSgYF35MD+41adB8epXCGXbFnVXTq1Li1JzAb17dh9QTr1WmRXm6qsZP7X0jk8qafWkDOfAfjXplNaNYSYrPjzmGG/QZ2Csb6XujXCk27Kqr9i/u6+eBGmJd9MShTNIpkzprBY1tP8z8KatiO+z0wZUkiZEo4rNRqfEWcBAADAkzksqff48auZMz4+kcv5/PXXX/p2zZo1LZ5/8+arkYhJkrDekjUmjFkugTdflfbLlSeDdP0gvDSmNb76ur0s+mOQjBrfSXoPaCSp00SfrDBR60Q42oE94SPIs+dMH+NMOpOSZV8zlAM9tP9cpGP27zmrbydPnkQrI2pV22XCyw+qjtJd209YdR4cJ1/2VJLSL3zGpiofOfKXfdLpqw1y75Ht5Zz6jtsm1Xsukx5jN8uoX/cbknXRSZ7UuvJnvskTG5J+as26uFDh9UyGz+zwaevK8+4+Gj4aXM3IrVoqS6Rj3ihqnDmy64h1a+XsjnCcWvMvofN2wgOejTjLsdS/7QEBflqpSFU2MmvW8BlG1kiWLPxzfGg2y84Wjx6FyDff/Krvq/X7hn3ZSxxp967D+raPTzLDLLnolCv3uuGz2rp1f7RtmxKSVrUd4bgtW2M30xG2U8m3lCnC1zcMCX0qw7/dIO17/Cb3H9heDUHJkyutJEuW2OFJvYrljIMUdu29aNV56vr37odXKqhRJerS4tZ6o2xOw2d26Ih1Cf1d+y4ZYqzqlfNE27byj5Xv07xtR71PT0CcBQAAAE8W/ptXLKVLl06uX78uly9fjrazqV69ehbPP3r01YyqTJkcU27Ik235+z/5649XJWXUuguDh7eWpElt+6NUo/mzZEujPax19fItCX4U/ot+thzGmU/2ePAgRE9OKvkKRE4kRCXisSeOXZFyFY3rgZw7c13fzp03k9Xr0+QrkDVS28ZiZnAVteadmm02bt5BuRpkf0ko5UpgsPawVo6M/pLCLLF44Xp4mdiICuZMbShPG1dJvQI5U+nbpy7d02bhWeNYhPst+lpaWbjeWKavgNlMxFv3QiXoXvRlPU1OX76vlSI1zfIr+lrsS2ABCQ1xlmOpOGjhom+17WfPnktwsHV/nyn37j2UO3fCy+mlSRNeHtoW06f/LkFB4X/3tmvfUHLnduygh1OnwhMD+fLltDoOKljImBg4cuSMtIpQnfTkyfDZ2mnTBkiGDNb93Z43bw7tPkyz/FTbiLsYa9Vfx+SbSVu1kpWxXZvP3LEYSkday3yWnnL0pPXJQpXYMyXLihTIqMVpUa2lbI2C+cIrDZw6c0ubhWeNiJ9F0cKZZcGS8HWblUL5wt+nWkcv6JZ18eqps0Hy7NkLfZafahsAAACAZ3NYUk+t8bJ69WrZsWOH3L17V1KnTq2XhLpx44a2nSxZMqlTJ3JJoUWLFsn58+f1tWIQtbu3H8k3I5bo+23frW717LPYmv/rFsN+5eqFY93mpfPGX8yz5bQ+UZglm3FNmCsXw9ftUm4FPZBHD8OTkNltSEJmyBQgiRJ7y4v/XxMtYttwPpUEWrHlvMxcdSzGdducpWtT43f87z3h6zNGVChXeMLLVH5TJQWb18gjb7yeSfJkTSn+Pknk3qOncvnmQ9n673VZuvmsXL8V4rD7TZ/KxzC78WI0SciIrt8KNiTecmWOXBoqT5aUViU4I1KJRdV+jkyv2sxptj5iQmWW/wWsQpzlPGqGXKpUkf/Oi8r69f8Y1oHLbWXZTnM3b96WObNX6fvq+r16Wbemn7VUacuHD8MTAzlzWt/ZnzlzOkPi7eKF8EFSinr/589fNWvb+kFZqt3MmdPL5cuvvrcXY1G+FPZR5SuX/XFEfpq9N8Z12+xJvj0KfiIXL9/TkmAtGr0uFcrkkJzZU0vy5Inlzt0QOX/xrrZm3OKVh+VeDOskv5YrPFmsEnKXLlsfE166ck9P6qmKHars5dXr4Ql5W6RP52eY3Xj+kvXLEFy78cCQeMudwxgzKnnM3ucFG9pWiUXVvvp8o2o7ISLOAgAAgCdzWFKvadOmWmdTaGio1K9fX8aPHy8hISHSrVs37XXVkaSe9/MLX5NNWbBggXTv3l3fb9mypaNuySN9PXyx3Lv7qoPmtXyZpEsPx627EpUXL17K7BkbZMXiXfpzaoZf/SZlYt327SBjYiBdeus7+9OmNXbABQYaRxjfvmX8pT1dBuvbVqW4Uqf21xKDSlCEtuF88/46pT3ighrJ3avl69K2XniZsks3HmpJuKgUyh3eifIo9Jl80qGUNK+WW5tNay5Dah/tUbpgBunZooj8vPq4TPjtcKxGjpu3be7mXesThqp/+vb9x9qad4rp/yYB/kklmVkp0sC71s9qUdSsPlNSL2LbAGJGnOUenj59Jj/9tNTwXM2a5WxuZ/q03+Xx46f6fteuLSRlSutKoVvLfBagYu1MOlMclCZNgJYYVG7eNA5uun//kfZZ2NO2ki5dKj2ppxKccK25i/7VHo5UuEB4Cdu790Llx/EtpG6NyOVeM2dMqT1UWc2+71eUCdO2y8/zoi7Bmj5d+M/FnXsh2gAka6kZb+YyxSKpl8HsPpSbQY9si7Huhmhr3pnuw1xAyuSG0qW2tG16n6aknukaAAAAADyXw5J67du3lxEjRsilS5dk7969Urly5UidA59++qm+r45p3LixBAUFaaN9VWdUsWLF5K233nLULXmcP1fuk22bjuqjnFXZTTW63BlJPPW4ef2u7Nt9WpYt/EfOnXnV8aKkSOkj30x+z7CejL3u3jH+0poypfWd/YmTJBIfn6QSGvqqU+zB/ZBo205hQ9uvjvfRk3oR24ZnUUm8xN5ekjm9n1Qqlkna1ctvKDV57+ET6Tpqkzx99jLa8psmakbeWzXD13yMSvJkiaVXy6JaqcteX2+R0CevZkTYK21AMsP+/UfhHcbWeBD8VE+4Bfgb20obED463fSZ2ML8XtRsQL/kiSX4sePX6IwvEjGCHDYiznIPU6b8ZphZliVLeqlevazNybYlS/42lK5s176RONqd2/cM+wE2zEbUjg/w15N69+4bY6o7t42DnQICbGs7ZUB4gkTNBlTrC/r7M+AjPjOfqZc9ayrtERM1823ox7WlSMGM8vEXaywOcEqXJvx7cT+GWX0RRVwfMFXKyGuSWsv8Puy9F1PCLeJ9OKJtEzUb0M83qQSH2BYDehriLAAAAHgyh2WEVMmnlStXamWfAgONJRVVR9Lo0aOlXLnwkcwpUqTQjlOvKTly5JBly5ZpnVKI7OaNe/L91yv0/Q5da0r+Qo5dd8Xk668Wyx/L91p8rWiJnPLJ0LclZ27j+hb2Cgk1JgZ8/YyJhJj4+IYn9UKCjW2FRvhl1va2w4+P2DY8y8ge5eXtWnktvrb/RKAMnrpbzl2NemS3+mssf45UFs+d8+cp+fdkkATdDRV/3yRSIn86aVU7n9QqG16urUqJLPJ174rSZ9y2WL0P3+TGRHtwaPgsCmuEPA4/XiXdjG0b921NyIVEON7PJ0mCTup5e8V+ZiYSFuKsuKfKbv44fbHhub4ftpOkSW0b5DR79kp58iQ8Rnn33Wbi42NbjGKN4BBjYsDPz7aEhq9v+GCOkAhrDgaHhMaqbT9f4/FqTUOSevGXKk2ZPq1xlrCyaftZmff7v3Lk+E25fSdYUqf2lbIls0mn1qWlXKnw5QNaNi4qd+6GysjvNkZqw9c3vKz4IxsTVSERjvczK1FuK/P7iO29RLwPR7Ztaj+hJ/WIswAAAODJHDrNq2jRonLkyBEZN26cbNy4UYKDg6VIkSLSq1cvqVatmuHYvHnzSqJEicTX11e6dOkiw4YNk4CAAEfejsdQI+xHDV2orw+Xv2BW6dS1llMTiJao2YGFi75al8JRnj01zkxK/P/reVlL3ZPJi/9f9yW87edRHmtr26Y1ZeCZMqeL3BFlWnPm4KmY11PMnSWlIemlZrqOnfOv/LzquOG4Ow+eyMZ9V7VH48q5/q+9+4COqtoaOL6DkEIKnUAgdEQ6iIDSEUERaSKKT8AGKggWLE+wgICiTxRFEX2KiqII0oRHUUSpIkgv0hRBQk1ISCGkEPKtc/im3ElmMjOZSSaT/2+tu7x35s65d3iZN2fOPmdveXPUTRL4//VVerarKXftPCULfraf4jMvgaWMn5/MLOdTVOnzL1sGQEz3ZT62+fxcdiH9lT7f5l5c/awDoJ9VmDZv3i3PPvO2oZbeLbfcKP363exSOypl5YLvVhsCZ3ffc6t4Q6ZVekz3+kGW77XMzMuO2y7latu23yn0s4qyRlar9JRLlzLl3xNXytKVfxgePxebIst/PKi34UPbyNinuupsCYo6VnX2Nv9+3PAa6/6Iq38ntqk6TXWD3RFolYI8v/eSo4/lwbbz+z4BAAAA+D6P526sWLGini2e54VLlpRNmzZJ8+bN9exz2Lfo219l+5Y/zSlVXpx8j8uDJ644ezr3oJ76gTnvq/Uyf84G6TvwRnniuT4SGJi/P6ErNgP9AS5WNbc+/4rVQJuSdcXYtmnQwFm255vSl8H/RNkJ6qlBl4f7NJIH72goc388LK99vl0ycglmqb+KRb/8JdGR4RIdGSpfrjiUI6Bna9nGY1I2PFDGD7OsrHn8rqayeO1RyXKzvl6Ov1nX4m6GwWpb19jkMbL9vOXFNqWWix9Hv1Pc3z/cRz+r4P366y55fORrhtV1NWtWlSlvPOlyW6tWbpSEBMvK7379b/Z4LT37/SDXBvoDHPSDcrQdkM+2bdpD0aJWli1ZsV9q6LSbZWTS1DWydJXjftAnX27Vq/seub+t+bExIzvIwAeNQT3r2sSu1h+27de4+lvAcB82nx+X78XB+flu2+Z98nuFfhYAAAD8m+cLsrnAOk0UcnfieKzMfHe5+fiBR7tL3fpVvXrNMWP7Sc06laV8+XBJTU2XQ3/EyP8Wb5Wff9yjfzSqbcn8zRJ3LlGmvPtAvn44XmMzk/RKlms/YrOsAiy29QXVCgXDuS6uWsrKyjLMKOcHsv969dOt8ldMksQlXtJpIZvUKa/Tcd7erqYeAFLbfbc1kMjypeWxN9fleP1fJ5Pk+Q82u3xdlZpT1e+rF301dWd0ZJjc0LCybNl/1q33kZWVv8Er68CdWqXoaKXdNS63bfysO6pPCMAz6Gfl36pVm+S5Z982rFSrVKmcfPLpBAkPz31CiCOLFq0xHN977+3iLfnuB1mtFlJ9LOt+UI62r2S53X/T7buYwhS+Zfuuk3pz1TszN8hdfZpK+XJXU6+2bhkt1aPKSMypREP/I7DENbkGv1zte6TbZAhxRc5+UAm3f/Pk3cfK3/u0bR8AAACAfymQ3BzHjh2T7du3y/r162Xbtm0SExPjsbbT09MlKSnJsKWnu1ZHylepwZfXXp4naf9f56ph42gZ/FBXr1+39U3XSuXIsno1YESZ0vr41f8MlslvD9ErBU02rv1Dvv/ut3xdKzg4MEdaKldkWKXYtF01GGxTX8z6XGekp1vOL5XPFYnwbZv2nJEz8alyOStbElMy9PFT0zbK42+tNwyM3NImWu7tUd+j11609qjh+KamVdxu65LN33hQoGv/Fx9k9fm2XZGYlp5l91yn2rZJLVXcB5xUTNTTG4ovb/Wzcu9jFZ86TV/OXipjnn4rR0Bv9uzXpEYN1ydYxcYmyNat+8zHTZrUk/r1a4i32Nbpy3Dxf7t0qz5ZqVKl8mjbtf5beobxXlytSwj/kJZ2WVasPmh4rF0bY6p/0+8gJSjItf54kE3/PSMfQT3r+8jvvdj+JvFk27m1XxzRzwIAAIA/81pQb+3atTJgwACpUKGC1K1bV88W79q1q7Rt21Zq1qyp67r07t1bli5dmq/rqBRUqi3r7b23Fog/+OaLtbJv93FzwEql3bSdiVmQOndrKqOf62N47Osv1jpM2ZeX8DIhhuOLKekuvT71ouX8iLJXZ/maj8sYj1NT0txuu4xN2ygeVm89oVNuWhver5FHr7H7yHnDcXRl99OwqYCktbAQY9A8L2GlLYOqF5KNn8ULNp9N63OdajuklGHCQlKqf0y+cFcJL2woXgqin5VbH2vKlI/F36mV+hMnfiSvv/6pXLFKCxlVrbJ8NWeK1Klb3a12f/hhk6E9lXrTm8qUMX6fpKSkuvT6i1bnly0XbtN2uMfaVmlBIyJcX/UI/7Br32nDsUrfae1CoqX/HhbqYr8mzHj+hcRLbt2j7X3k915s2/Jk27qPZdOHK47oZwEAAMCfebx/qmZx33nnndKtWzdZsmSJJCQkmFM2Wm/JycmyYsUK6d+/v9x11136PHeMHTtWEhMTDduTz90lRd2fh0/JZzN/NB8/PLKH1KoTKYWt38AbpUpUOfPx6ZPx8s+xWLfbq1DBOCh0ISHF6dcmJaYaCsmXK28cvCqfo+2LLt1b/PlkS9vlvFPvBr7vmx8PS8w5y99ljchwqVMtwmPtx10wDjCVi3C/9lVsgrGt8i62VbFMsHn/vM0A0/kLaYYaL+Ujgl1ru6zl/AspGS7XiwFQ8P2s3PpYY8c+Kv5MBacee3SSfPP1CsPjakXdN9+8IbVqRbnd9tpffjfvq1SW3bvfJN5UsaKlv6bEW9Xyc0ZcnKXGcoXyxkBLxYplDek44+Pdb7ts2fAc6TxRfMSdN/bPy9tMpIs9b+mDmdJ0OqtieWOwOC7etd8ChvuIM/5Gyc+92N6H+jcw9LHy0XZC4iX6WAAAAICf82hOwZSUFOnUqZPs3bvXsHpL/ehXs7tDQ0P1IJMakFJM5yxevFiOHDkiv/32m4SEGFdu5SUoKEhv1tLTin4Kn3Vr9kmmVXq6me+u0JsrBt4+xXD83YqxUrVa+Xzdl5pN3apNPVm+xDIwdfzvc1KzdmW32rO9n9izlhoaeTl39oKxrShjWyr4qGqKmX7YnnOh7YspaYaVevn9d0PRpf5vavPeM7rGnokK6h096doApj22tRrzk5byVOxFPUPbtKK3SoXSLq2kCyttmeltHcjU93X5ipxLuGRu05W29fnlLeefOOt88N5fUaITRaGflVsfK1tcW0FSlJw7Fy/Dh02QQ4eOGR5v3aaJzJgxTiIi3J/gk5aWLr//vt983LRpfYmMrCDeVK1aZd1vM60OPHsmzqXg5sWLloki1atH5kiXqVKRqn8z3fZZ59u+ev55u22jmLH5PrTtB504mSitml9dHRseFqRXsaVcdC6VbNVIywQ/9Xvg5Cn3+24nTycZ+ljWbedF3bO6d5OYk4k53vO5uBSpUjnc5bZtz7dtu7iinwUAAAB/5tGVesOGDZM9e/aYj++55x5ZvXq1HlyKj4+XEydOyIULF/Rs8VWrVsnAgQPN5+7bt09GjhzpyduBE9SAX9ol12qs2K6Ay08Nw6jqFQy17479fc7p1x7/27hCsE49Yy0ylbK0WnRFq/Ndadt4bp16DDj5k2Cb+m55ibtgXLUWbFO7JLBkCalaobQ0qVtebmzi2t9KBavVcUpCPlImqcDb8TOWgFnd6saVFY7Yrj48/E/OQaEjJyyB9FpVw50eMFErBsuGB+XaDgDn0c/ynpiYs3LvoOdzBPR63dFJZs16NV8BPWX79j8M9Qi7dm0t3qYCb9a1//76y/lai0ePGs/NrfZf/Wsttc+OHTtlSC3qyPnzFyQx0fJd5c26gigYwcElpXpUGWnRpKpc3ywqX6vp4hOMqVwP/2ns79er7XwwvG4ty7knTl6QSza161yhAm/HTiS4dx825x6yeU+277N2zfJO97EqlCstZa3KGeTWNgAAAAD/4rGg3pYtW2T+/Pl6tria1b1s2TKZO3euTg+lZo5bU7PJe/ToIfPmzZPly5dLcHCwDi59+eWXsm3bNk/dEuxITrokYx77RO7t8x/p1macTBk/36XXq7SX1mzTXrpCzXZt0MhSm+bQHzFO1+j7Y+8/huPGzXIOCjVqGm3eP3E8VlKSL7nZtmXgCkVPeOlS8vnLN8uP0/vI/rn3yhuPu5byrIxNTRbb1JQb/nun3pb853b57KWbJbCU8//X2rSecaBn/9Grqx7ctfuIZbVE7arhTte+a17fEgBXdh3OOSi0y6rt0JBSUs/JoGGLa23bdm1Fhz8K8MIG/0Y/y3tOn46VoUNflJMnjRN6Hnn0Lpk69RkdHMuvHTsOGI7btm0qBaF582sNgbfkZOfSD+7efdjYTosGOdtuZmk7NTVN/vzzhMfaRtERElxK/vj1Gdm4YoQsmXO/fPCffi69vllj46S8fQfPGo537jXW3GvW2BKodkStjKtTy5JpY+feU5Jfu6zuRQXerFffOdKiiTHQmdu9WL/P0NKBUr9ORefabpp328UR/SwAAAD4M48F9b766ivz/gcffCC9evVy6nU9e/bU55t8/vnnnrqlIu3hET1k4+63XNoefKx7jnSb1s+bUkiGhQfL/r3/6CBXRsZl2br5sEur7Xbv+Nu8rwYXa9ZyL/WmSeubrjXUvftjr3ODQps3WAbIqkaVk+ialXKc08aqbZV2Z8umQ061/et6S9tBwaWkWcvaTr0Ovik5NVNa1K+oV6MFBV4jHZpXdSnw1rpRZcPf0VGb1EYHj1tmbgeWukZuamocoHLkthuNwejf9hkHs1y1cfdpQ9C8UwvnZszffEM18/6l9Muy7WDOoN7GXcaBta6tLK9xpGur6nbvsbhSM/A9vcG/0c/yDpViUqXcPGUV0FMpK1955TEZM2ZojhTJ7tq1y9L/KFWqpDRpWl8KQvv2Lcz7aiXdhg07nHrd2rWWNOvBwYHSqlWjXNpuaThet3aby20r7dpZ7hFFj1r99k+MZQV+VJUIqVfHuVVsJUuWkO5d6htWw23fZVwlqo5TrbKK3NyxrlNtd+1Qx5wqU9mw2fL7xV3Wbai2O7dz7vdBt06We750KVN+35lz1azt/Tn7Pq3bVjb+ZlxtXFzRzwIAAIA/81hQb+3atfq/1apVk4ceesil1z744IMSHX11RdW6des8dUuwQw1Q3dC2nmHl3o/LdzgdSDt21BJ0uK5RdakU6XyKv9zc3KO54Xjh3I15vkYF5/45Zgk69Oh1fa7nte/cyJDec+G3v+bZtnp/27YcMR936dZUB/ZQtG3ae8a8r1JB9utUx6nXdb4+SupHlzUf7/3rvJyNN674/GXbScPx4NucW3XQ5fpqhlVsv/9xTv6MyV8tlDXbYiQ17bL5eEjPvO+lbrUIaWcViPzht38kPSNnbb8dh2J13T6TQd3rS6mSjr9GyoUHSe8ONQ3v8aRVGwCcQz/LOyZN+tiwwkwN1L/55lPyr/tu9+h1Dh44at6vVbuaR1b/OaPrzW0kJMSymujrOcvzfM1ff52Qzb/uNh/36NFOgoNzrkhqef11UrWq5Tts/vwfJCPD8SSxhPgkWf6/9eZjFSykpl7R9/OGPw3HQ+/JvV9u694BLaRqpCX99/9+OCBJNmnIVaDvx18s/fKON9WWOjXzrnV9/72tzPsqKPjDz8YVou5Ys/5PQ4Bx6CDLNRyl3mzftpb5eOWaQ5KebumnmWzfHSMnTyca/m3y7GOVDZHet1kC7lt3nJCYU9TUAwAAAPydx4J6J0+e1MGi9u3bu/X6du3a6dRQx44xu7Ag9BtoTD/48fSVcvaM4zpXp2Lic6TqHPxQ13zfS41alaRte0vgYfXKXfLzj5bBJFsJ51PkrckLzceBQSWl78Abcz03LDxEbutt+cG9Z+ff8s3sqwOjuVH1BSe9+K1ejWVy1786uPR+4Jvm/mAczHnmvha6Dp4j1SuHyhsjbT4ri/fnOG/phr/l4qVMwwq23h0sAzi5qVklTKaMNP7dfrhwr+RXSmqmLF531LDK8OE+DR3WF5z6RHvDbPbZy3Nf0aoy485ZZXmuRpVwGfeA/QEtNav5zVE3SVhpS/rS2csPuvR+/FUJL2zwb/SzPG/NT7/JksU/Gx57Yeww6d2ni0evExeXIOfPWwba69Yxrl72pvDwUOnbt6uhtt+sWYvtnn/pUro8/9w0Q328IUN653q1MnhIAAAxgklEQVSuWtH4r39ZVoyeOHFG3nhjlt22VZsvjH1Xr440tz0097ZRtHy7aLdkZVn+Zu69s4W0ud6SAj83qv7euKe6GoJ3H33+W67nzp673byv+ivvvHaHBAUZ6xtbe+yBttKqueVz9t33eyXloms1xHOTnJIui5btMx+r9zh8aBuHtQanTb7D0Mf6Yu42u32sr+ZZJlnWjC4nLz3bzWEfa+rEXoYUoJ9/Q3plE/pZAAAA8Gce65+mpV2tMaXqtrjD9DrrQQR4j0p52bFrY0Pay1EPzdRBr9ys+2mvPDJ4usSfTzGsgut8i/2aMB2aP2fYTp+0Xyvs0dE95Zr/n42qBh0nv/StLFu0JddVdE8M/0jOnLKkOxw0tLNUjrSspLJ1//BbJDTM8nf50bsr5Iv//mQYfFBizybKMyM/1XX9THrc3lIaNnE8KIGiYdOeM7J6q2U1RoUywfL1pO5yfYOcaVuVHm2jZeEbPaVSuRDzY2t+j5Eft+RMDxuflC4zFhgDciqYdX+vBnJNiZz5errdUF2+nXyroe0FP/8lG2zSW1pbO7Of/LlwsHlr29j+yoYPF+yVZKvBq+cHt5SRA5pICZt7iSwfIp+9fLOhrt/36/7WqxHt+WrlITkZm2JYCTh+WOsc6UxVHcP3n+0kN99Q3bBKb9VvxnqVxVVAQLbHN/g3+lmepfoa778/1/BY9x43yZAhd3j8WsePGf+/PTLSudSEjtx88zC5rkEf87Zli/1JISNG3iNhYZZJLG9PnS0zP5wnWVnGFdlnz56X4cPGy/79llVXvXt3lqbN7KcKvW9wL4mKsnyPfvP1Cpk48SNJTzcGUJKSUuTJJ980pOhUq/Ruu829IDV8y+G/4mTuwl3m41KlrpFZ0++SPj1zpm1VBvZtKnM+HiQhIZYVqzM+/VW3kxtVJ27F6oOGGnVfzrhbIisb63qrfs7o4e3k+ScsgfkLiZdk+sebHN6/qgd4bNcL5u3GG3LW6TZ5/9NfJSnZUlv5hSe7yKhh7XL2sSqHyewZ9xhqAC5evk/27LdkjrA1+9sdhpV29w9qJa++0F2njrcWER4kH77VX7p1qmdYpbfyJ+fKDBQH9LMAAADgz+xPcXRRpUqV9CzyQ4fc+zFhep1qBwXjhQkD5eSJ83L0z6s/LlXQbeQDH0rDxtHSuFkNCQkNkvi4ZNm57S+9Ss+aCnRNeOM+j93LtQ2ryYgnb5cP3v6fPs5IvyxvvrpA5s5eJ63a1tP1XNR9/r75sGEVXZPmNeWhR421BG2p9KD/Hn+XTPj31/q1avt0xg+ydOEWubF9AwmPKK3rC/628aCuMWhSLbqCjBnX32PvEYVv7IzNUiMyTBrULKePa0SGy/zXb5XdR+Jk1+E4uZiWKZXKhuiAmVqFZk09/9S0DXbb/uT7P6RZ/YrmGnmqtt7LD7WWYX0a6RpysRcuSdmwIGnTOFLqVTemrN2465S88t+cQWx3qfSg42b+Ju8+3UHPDlfbmH+10Oky1+08JUkX06VW1Qid/tN6oOj46WQZ/+lWh21fSs+Sp6dtktnju0nI/8+SV4G9Xu1qys/bT0rchUsSVSlUB/PCrAbr4pPS5JnpjgfVANhHP8uzNm3aJQcPGicyrf5xsw6QuePgoaV2nzt12lijtGLFq99BBUUFESdPHiVjxkzVQV21vffe1zpdZsdOraRMRJgcO3ZS1q3bbkifWaNGFXll/GMO2y5dOlimTn1GHnroFUlLyzAH9lau2ChduraWihXLyqlTsfLLz79LaqplhV7ZsuHyn7fGePFdo6BNfudnadigsnmFnFpBNn1KH3ny0fby2+//SELiJalUIVSnoqweZewHLVi6V6b/13Ef4aXXf5BGDSKlVo2rn5+2N9SQ9csek583/CXH/omXMmVCpEv7Orqmn4mawPfsK8vlfEKqx97n2XMp8sLElfL+G33NfaxnR3WSewc0l3WbjkpiUprUrlleunaoa1hNeOyfBHllyuo86xM+OW6pzJlpCXiqwN4dPRrqFKexcRelWlSEDuaFhVpW6MUnpMrTLy7z2HsEAAAAUEyCejfccIPExMTI1q1b5cCBA9Kwof10b7b2798vW7Zs0WmlWrRo4albQh7KlA2V9/77qEwY+7Vs32KZlX1g/wm92aPSWT774p0SHGJJqecJasVdZmaWfPLBKnPgTtXNs66dZ61pi5ryn/cflpKljLNX7dXtuxoo/E5fQzl35oIO7NlLCfrOR8N1+k74jwspGTJkwk8y7ekO0r6ZZeZ08/oV9WbPol/+klf+u1XScqkzZ502acy0jXJhWGsdPDOpWjFUBnazzKTOLXXn2A9/k4xMz66eWbn5HwkK3Cyvj7hRBxgVFWy7t0fuKy7+OpkoD0xco9N35kXV1nv0jbXy4XOdzKk1y5cJlrturpvr+ecSLslDk9YY6vEVdznXbwKO0c/yLOsVY96WEG+scVU61L3VlvlxW88Okp6eKS+99L5kZl6dwHT6dJzMn/dDrufXrl1NZn02UafvzMv1rRrJhzNfktGjpphTayYkJMniRWtyPb9SpXLyyacTpFq1yvl6T/AtaWmX5cFR38nbE++Q7l0tfY26tSroLTcq6PbF3O0y+e01uh/lSHzCJbl3+Dd6hZ+pPRU063lL7rWD0zMuywuvrpSf1hnr/XnCitWHJChwubwxvqcEBV79OV2tahn5110tcz3/r7/Py5AR83T6zrxs33VShj21QD56+05zas0K5UvLwL7Ncj3/XGyK3P/4fDl5Oilf78nf0M8CAACAP/NY+s2+ffua0xkNHTpUUlIs6dkcUeep89XrlD593JshDfeUqxAm7378iLw46R6pU6+K3fPULNQbbqwv0z95VF6aPMjjAT2TIQ/fLB99OUpatKqjBx9zU6FShIx8upe8P2uEhEc4H3RTwcjZC8ZIu04Nzak+bakgnqoT+Nm3T0mVqgU7kx4FQ6XKvP/VNfLc+7/KoeOWNK62Lmdd0SvsBo9fLc9/sNlhQM8k4/IVeemjLXL/qz/J5r1nDKtKbQextv5xVge6xry7SdKdaNsdS9b9Lb3GLJeft8VI5uXcg4aJKeny0aJ90vfZFXI6zvmZ7L/uOSM9nlim03WmpVtWuFpLTbssX/9wWHo+tUwOHndcsxOAY/SzPOufE/bTHXtaaqolVZ8SFGhZxVyQ+vbrKt8vna5X0JUsmfuEqIiIUBn+yABZtPhdQ1rNvLRr10JWrPxQp+sMCsq9jxgSEiSDBt0my/73gVx3XW233wd8V1Jyugx/eqE8/vwS2bPf/mdM1c/7ZeNfcuf9X8mkqXkH9ExOn02W2+/+TN77eJNenZYb1fdSq9p63/uFLF6esw6yp6i2ew78TAcNTRMGbalVex/O2iy97v1cTp1xPui2actx6db/E52uMy0t98lWqZcyZM78HdJ9wKdy4PA5t98HAAAAgKInINs0ypNPmZmZcu2118o//1ytl6T2p0+fLt2720+N+MMPP8hTTz0lhw8f1sfR0dFy5MgRKVUqf4MdsWn2UyDBMZVmc//e4zrtpprRHRFRWipXKSvNWtYq8FVrcecSZffOY3I+Nknfiwrg1WsQJdc1qm53MMpZiRcuyq5tRyX2XKKkpqbr91a7bqQ0alpDgoIKZ7CtoNx0HzN5rVWvHCotrq0klcoGS3DgNXo1nwpubTt4zqlVa46UCw+SVtdVkioVSusac4kXM/SqNZXKM+6CcZDX28qGBerUn5HlS0tYSElJupgpR05ckF1H4vK9SrB0cEmdsjSqYqhEhAXqf7e/TyXJzkOxcjEt94CfL1M1C71td/zVVMOe1Ly852uBwXf4Sj8rW6jZ5A/USrrff98nZ8/Gy8WLqRIRHib16teQ5s2vtRuUc5Zarbd16z45fSpW19ILDSutV/61aNHAUNvPX9Vusbiwb8FnqLpyrZpXk8oVw6R06UBJuJCq01du2xWjA4D5oWrYtW5ZXafjrFCutJ4gpQJnqm2VprIglS0TLG1b1ZAqlcMlNDRQ19w78lec7Np7Kt8Tt0qHlNI1/qKqRkiZiGBJScmQo8fPy47dp+RiqrF2ZVGh6hZ6G/0sAAAA+DOPBfWUDRs2SLdu3SQrK0vPCFcrraKioqRNmzZ6ICk0NFQuXrwoJ06c0GmgTp++OoNTnRsYGKgHnzp37pzv+yCoB9hHUA8o/KDeHi8MNjVjsMnv+UI/i6Ae4BhBPaDwg3r0swAAAODPPFZTT+nYsaN8//33cvfdd+tBJTWIdOrUKVmyZEmOc61jieHh4fLFF194JKAHAADgj+hnAQAAAAAAFG8eq6ln0rNnT9m7d6+u36LSO6lBpdw2Rc0aHzJkiOzcuVP69+/v6VsBAMAnlQjw/OYu9T2sVnw5uzVp0sRhe/v27ZPHHntMGjRoIKVLl5YyZcro14wZM0YOHjzo/o1Co58FAEDR6WcBAAAAPp1+05aaRb5+/XrZtWuXxMXFSXJysoSFhUnFihWlZcuW0q5dOz3Y52mk3wTsI/0mUPjpN/cleD4tVJNy7qWFatasmQ4SOatx48Y6cJebKVOmyMsvv6zTQ+ZGBaFef/11efbZZ926VxR+P4v0m4BjpN8ECj/9pi/1swAAAACfTr9pS9V2UTPK1QYAAK7ylQnfGRkZ5tVzKvgzaNCgPF9TrVq1XB9/8803Zdy4cebjmjVrSvv27eXy5cuybt06OXv2rGRmZspzzz0n11xzjTz99NMefCfFE/0sAAB8t58FAAAAFKmgnpqlv2HDBlm+fLns379fD+YlJSVJpUqVpEqVKnLDDTfoVFANGzb01i0AAOCTAnxktOmPP/7QgTalbdu28tFHH7nVjlop9uKLLxpW7D3//PNSokQJc/BQBfzefvttfawCe927d88zlSfso58FAIBv97MAAACAIhPUmzt3rvz73/+WkydP5nju6NGj+r/ff/+9TtHVokULeeutt+Tmm2/2xq0AAAA7du/ebUjD6S4VsDOl3Bw1apS88IIxtZaq7TZ16lQddPrkk0/0uRMmTJAFCxb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      "text/plain": [
       "<Figure size 2200x1200 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.model_selection import KFold\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# shape_train = (train_data[:, 1]==0)\n",
    "# y_1=np.zeros(sum(shape_train))\n",
    "# shape_test = (test_data[:, 1]==0)\n",
    "# y_2=np.ones(sum(shape_test))\n",
    "# # from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "# # from sklearn.preprocessing import StandardScaler\n",
    "# # from sklearn.svm import LinearSVC\n",
    "# # from sklearn.feature_selection import SelectFromModel\n",
    "# # from sklearn.svm import SVC\n",
    "# # from sklearn.ensemble import RandomForestClassifier\n",
    "# # from sklearn.calibration import CalibratedClassifierCV\n",
    "# # from sklearn.model_selection import KFold\n",
    "\n",
    "# X_pre=train_data[shape_train,2:-1]\n",
    "# data_pre=np.concatenate((y_1.reshape(-1,1), X_pre), axis=1)\n",
    "# X_post=test_data[shape_test,2:-1]\n",
    "# data_post=np.concatenate((y_2.reshape(-1,1), X_post), axis=1)\n",
    "# np.random.shuffle(data_pre)\n",
    "# np.random.shuffle(data_post)\n",
    "# # 5-fold cross-validation\n",
    "# kf_pre=KFold(n_splits=5)\n",
    "# kf_pre.get_n_splits(data_pre)\n",
    "\n",
    "# kf_post=KFold(n_splits=5)\n",
    "# kf_post.get_n_splits(data_post)\n",
    "\n",
    "# for (train_index_pre, test_index_pre), (train_index_post, test_index_post) in zip(kf_pre.split(data_pre), kf_post.split(data_post)):\n",
    "#     # get training set\n",
    "#     train_data_pre=data_pre[train_index_pre, :]\n",
    "#     train_data_post=data_post[train_index_post, :]\n",
    "#     train_set=np.concatenate((train_data_post, train_data_pre), axis=0)\n",
    "#     # get test set\n",
    "#     test_data_pre=data_pre[test_index_pre, :]\n",
    "#     test_data_post=data_post[test_index_post, :]\n",
    "#     test_set=np.concatenate((test_data_post, test_data_pre), axis=0)\n",
    "    \n",
    "#     X_train = train_set[:, 1:-1]\n",
    "#     Y_train = train_set[:, 0]\n",
    "#     X_test = test_set[:, 1:-1]\n",
    "#     Y_test = test_set[:, 0]\n",
    "\n",
    "#     sc=StandardScaler()\n",
    "#     X_train_norm = sc.fit_transform(X_train)\n",
    "#     X_test_norm = sc.transform(X_test)\n",
    "\n",
    "#     svm=LinearSVC(penalty='l1', C=1.0).fit(X_train_norm, Y_train)\n",
    "#     Y_pred = svm.predict(X_test_norm)\n",
    "#     print(accuracy_score(Y_test, Y_pred))\n",
    "#     print(confusion_matrix(Y_test, Y_pred))\n",
    "# All_feature\n",
    "accuracy_score_list=[]\n",
    "confusion_matrix_list=[]\n",
    "\n",
    "X_pre=train_data[:,2:]\n",
    "shape1=train_data.shape[0]\n",
    "y_1=np.zeros(shape1)\n",
    "data_pre=np.concatenate((y_1.reshape(-1,1), X_pre), axis=1)\n",
    "X_post=test_data[:,2:]\n",
    "shape2=test_data.shape[0]\n",
    "y_2=np.ones(shape2)\n",
    "data_post=np.concatenate((y_2.reshape(-1,1), X_post), axis=1)\n",
    "np.random.shuffle(data_pre)\n",
    "np.random.shuffle(data_post)\n",
    "# 5-fold cross-validation\n",
    "kf_pre=KFold(n_splits=5)\n",
    "kf_pre.get_n_splits(data_pre)\n",
    "\n",
    "kf_post=KFold(n_splits=5)\n",
    "kf_post.get_n_splits(data_post)\n",
    "\n",
    "for (train_index_pre, test_index_pre), (train_index_post, test_index_post) in zip(kf_pre.split(data_pre), kf_post.split(data_post)):\n",
    "    # # get training set\n",
    "    # print(train_index_pre)\n",
    "    # print(test_index_pre)\n",
    "    train_data_pre=data_pre[train_index_pre, :]\n",
    "    train_data_post=data_post[train_index_post, :]\n",
    "    train_set=np.concatenate((train_data_post, train_data_pre), axis=0)\n",
    "    # get test set\n",
    "    test_data_pre=data_pre[test_index_pre, :]\n",
    "    test_data_post=data_post[test_index_post, :]\n",
    "    test_set=np.concatenate((test_data_post, test_data_pre), axis=0)\n",
    "    \n",
    "    X_train = train_set[:, 1:]\n",
    "    Y_train = train_set[:, 0]\n",
    "    X_test = test_set[:, 1:]\n",
    "    Y_test = test_set[:, 0]\n",
    "\n",
    "    sc=StandardScaler()\n",
    "    X_train_norm = sc.fit_transform(X_train)\n",
    "    X_test_norm = sc.transform(X_test)\n",
    "\n",
    "    svm=LinearSVC(penalty='l1', C=1.0).fit(X_train_norm, Y_train)\n",
    "    Y_pred = svm.predict(X_test_norm)\n",
    "    acc=accuracy_score(Y_test, Y_pred)\n",
    "    cm=confusion_matrix(Y_test, Y_pred)\n",
    "    print(acc)\n",
    "    print(cm)\n",
    "    accuracy_score_list.append(acc)\n",
    "    confusion_matrix_list.append(cm)\n",
    "classes=['pre','post']\n",
    "fig, axes = plt.subplots(2, 3, figsize=(22,12))\n",
    "axes=axes.flatten()\n",
    "\n",
    "for i in range(5):\n",
    "    plt.rcParams['font.family']='arial'\n",
    "    plt.rcParams['font.size']=25\n",
    "    sns.heatmap(confusion_matrix_list[i], ax=axes[i], annot=True, cmap='YlGnBu', fmt='.2f', xticklabels=classes, yticklabels=classes, annot_kws={'size': 30})\n",
    "    axes[i].set_title(f'Fold{i+1} ACC: {\"{:.2f}\".format(accuracy_score_list[i]*100)}%', fontsize=25)\n",
    "\n",
    "axes[5].axis('off')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "f824215e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7804878048780488\n",
      "[[155  36]\n",
      " [ 45 133]]\n",
      "0.75\n",
      "[[141  49]\n",
      " [ 43 135]]\n",
      "0.7853260869565217\n",
      "[[153  37]\n",
      " [ 42 136]]\n",
      "0.7934782608695652\n",
      "[[156  34]\n",
      " [ 42 136]]\n",
      "0.776566757493188\n",
      "[[159  31]\n",
      " [ 51 126]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(np.float64(0.0), np.float64(1.0), np.float64(0.0), np.float64(1.0))"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d2bp9vwx/Z7YEE3EWQtmftXhxYIPMe/d+1ZT/tPTp0ykoMwLpzwJCI20wSxUoDWhGjBjh17mvvvqqqfluBaWpkY4E0tE0vbs1T1I7N1ZyDYL0izlYbqjk+g/pHxt3SXK4s0EV+V/LWvbtVWv/khpNn5fPvlhh6oOfv3DJXNuTL06Uto+8ZX856lo0b73UOdHRSpXKFTfBjWX7roNSo+lzphzBvgPH5MKFS7L1739k8OszTIAZe+a8feyrAzqYkWUIr4ED33MZsd2z5wMybtwgE9zoF7gGCOXLl5T+/bvJ6NH97Z+/BgkvvPCOx9HeOkpTyx84E3o9evxPPvvsdWnQ4BZT8ztTpoxyww1lZfjwJ2XYMNfF0UeMmBi0keQagKxdG//HTMOGt8r06a9Lo0Y1JHfuHJIlSyaTbHzrrWdkwIDu9nFnz56XV14Zn2jbP/yw2g6c1I03lpM5c96Rli1vN+sD6musUKGUDB78mIwe/bzL+/fSS+MCKrUFRjbBf8RZkRFneaKdgB+OfNSMvA6mZ3vdLZPfe0IyZAj6MtgeEWfBV7ru3YDnx3udfebOkAETXRJ6hQvnlemzXpJHe94tJUsVkQwZ0kvefDmleYtaMmP2S3Jb/ZvsYzVBNuOz4JVEW7Z0nXz7zSr79vU3lJLps4aY59ZryJQpg5SvUEL6D+wkI9/p6RILDR86JdFY6K+te1ySnsWK5zevR8trFiyUx8Sp+np7PXmvTPzkOcmcOaN9rCYNT58ObB2lVC8Ecdbzzz8vJ0+edNn0vuS0YMECadq0qZw5c8aeVafrulWuXNlrKc3Ll68kkL1xzs7LmDH+8xltiLOiuz/rud53S5lS8TOOvl2yTurc1V8+n/eTHD56Si5d+ld27Tks7074Ruq0eFH2HzpuH9v/yTZSvkzwyp8TZyEc/Vn++vDD2S4JvSZNassjj9wblLbpz4pC9GdFV1Jv48aNpkOjXr16kj37lVEtvsqdO7fceuutJmNuLTicmtxQsYSs/GqYtG52a5Lbco5s2r33sJwI4uK+zvX0Ll68LJsCKLMTDFrf3KJByAPd35IzZ92XPpn33a8ycMRn9m0d8dTh3noe2+7/VBuXf+Tb9Xjb48LBugBz935j7ds6i9EZQCH0fvtts1lc16KBT69e7Twer8m49u3jf0Zal1xLHbjz1VfLZP36+EW2W7duIE891clj223aNDKBg+Xw4eOyeHF8B1GgdMT2mDHT7duFCl2pL+5pceQOHe6Sdu2a2be1bvhPP/3usf1Ro+LLO2kw9e67z0vWrO6D+UaNakqfPg/at3fu3Cdz5izy+zXh/799g70hqhFnRUac5clT3e+SWtXLB609HSH+xaRnzToq3konBRtxFnz11hvTZc+eQ36ft2H9DvlxWXzJTU2avf9BX4mJKeT2eO3Qeu3Nx6Sko0P2/XfnmDXwgmHM6C/sfU2qvflOL8niIUHfoFFV6f1k/O/Irp0HZO6Xyz22Pfb9L+2kp64z9cZbPSVf/lxuj73hxtLy0rCH7Ns6sGxSALMgEZo4SxNamrhxbsmZ5Jo0aZK0bNnSrOmm0qVLJ9OmTZPmzT0nVZxr7Gm5SV9YCUPlb/yRkhBnRW9/VuZMGeSRTvGzB48cOy0P9nrXJM/c2bn7kDzxwkT7tsYt3TvdIcFCnIVw9Gf5Y+PGv81ac5YiRfLL0KE9JRjoz4pS9GdFhKC9bceOHTP/L1KkSEDnFy16ZeTL2bOpZzSidtjoSJvl84ZK6ZLxf6Tq6JxA3eAY2fRHEGfpqRsd6+lt+fsfM5op3G6qHCNVbyhl3/5kxg/yz8H4UVTujPrwa9m3/6h9u0vbq2viqwL5cspdjeMXvP9m8W/y+4adibY9+6tfZPVv8YmfB+8PTrlF+Gb+fNeRkL6MJNLkm9NPP61ze5yzxraOHtKRUd506uS63tGPP66RpFqyZJVJEFo6d25lgpXEPPFEB9NBZvn8c/drvmzYsM1szvdGZyAmRp/feczMmb6tJwMgaYizIiPOcqdi2WIywNFBs8BRZs9f6dNfIz0faip/LB0pTRwzk7TT58zZ4CQwEkOcBV+tXLHeni2nyaqatRMvQen05ReuSbDOXZuaMpaJ0bimZ+977NvHj5+Wr7/6WZJK1/Xb6PgcarnLgo7ybO50eLCJFHAcM8fDcgRHj5yUHxz/HtS97Uap4GV98jvuvEUqXx//O/jF7B/tUlGAZdiwYdK5c2d7Fl2GDBnk888/lzZt2iR6Xp48eez9o0fj/91OzJEjR+z9AgVCs9ZZJCDOit7+LI2ncueMT2jrrLWTpxL/Oc3/fo1J/lka1PH9Oy4xxFkIV3+Wr3Smn1aossp/q1deeUJy5Ij/nUkK+rOAFJDUy5kzp/n/8eOJfyF5cuDAgasCzWjWoO71sn7ZW9KjcxOXEQpLVqyXVp1eDahNHZFTsUwx+/afQS6Peb3jj9A/NyVP6c277qjmcvvzud4XTtURShqsWKpXKS3FCudxG+w5fxYzfWhbTf8yfoq61mGvVb2cT+ch6X7/Pb5utpYmuPXW672eU7Kka+mMPXsOXnXMrl3/yJ9//mXf7tixhU9BjdY81/Z109rjefJc+XcxKRKOvGrWzPvaKrlyZZc6dW62by9ZslouXLjoQ9ueR/1ZtOzHnXfWtm///vtW2b//sNfzkADlCuAn4qzkj7M8/Zuo691Zf3jOmv+z2QKh67ysXfi6vD6ok8saxloOqsE9g+TI0fjOpVAhzoIvdAbZ4AHxsxg6db5TbrzxOp/PX+1YH0lnxmgpSl/c3uBml46g779dLUm1ZJFrEl7X8/Pl9/4Ox3pFf/6xXQ44OlwtOhvRWdbpzqa+zWJpdlcNe//woRNm3UL4KUrjLF0eoEePHvLiiy+6zL776quvzLp63pQvHz+jfM+ePV6PP3/+vEtSLyYm8XWNUjLirOjtz9K4JOHsN290MMX2nVd+piqmeHAS2sRZCEd/lr9lN//6Kz6Bfs89Dd2u0xco+rOiVJTGWak2qVeqVCnzxbd8+XL591//ZnCdOHFCli1bZv6o03ZSAx3VnStnfE17HX39zJBPpHm7V+TYCd9KYSSkU/GdoxmCmXjLmiWjlLy2QMgShr66vVYll/ds9br4Be0Ts+ynjfa+LjR8h2P0u912bdcFYJc6zvG1beUcWY/QmjHjDfnmmzGm9vazz3b1acH38+evDgYSWrrUNdBv1cr9aDh3FiwYa7apU1+Vvn3jp/YHSssNWGJiinodeWS55Zb4gPDs2XOyevX6RNvWclO6PqC/beu/+8uWJX1GYqqTJgQbohpxVvLHWe688MQ9UuX6kmZfyxs90T8+0eGv/HlzuIx0VxOnLZZb7nw2qGskJ4Y4C754ddgUOXjgyqyWUqWKyOOOGXS+JCV27txv39YZerpunS+0I8Y50+33ddtMe0mx6pf4BGOmzBlcZsklppqj3K75t/nHP9207frZrX5rBb/bVssdpUqReuMs/e7v2LGjjBs3zr4vf/78smTJEmnUyHXmhicVK1a09zdtiv/se7J582Z7pmjhwoWjOmFFnBW9/Vn9X/lUrqv+mLR+cIQMGPGZz+06ryVYiLMQjv4sX+3de1DGjZvhkkx79tn4MuDBQH9WlIrCOCtVJ/XuvPNO8//Dhw/LmDFj/Dr36aeftssU+BqQRgv9Q3T6Fyvk5kZPm0V5k1JexVkeU/2+YVdQ66Rr8GC3nUxJPV3417Jxy14zaskXutCw082OkgeWyuXj1wzUjrkDh0741PbGrXtdpqq7axuhoZ/JUqWKmdE+993XxOd64U4lSrh2oCrnIr76eLFiBSU5nDwZKwcOxI+OrVDhSsexLxIe61wf0LJ1a/zvRZkyJUxnmW9tl/LaNoDgIs5K/jgroWo3XidPP97Kvt37hQ/l6PHgzKZbtfYvaXTvYHn82fFy6vSV9ZLCgTgL3iz8/lf5at5P9qyKl4c/LBkypPf5/FOnztprzKmixfL59fx58+aw98+du2DWtEuKbX/Fz1YqXbqYz7FQOcfnWW3ccPU6Wn9tjV9/PE/eHJLfw1p6CV1XuqjLdWxw0zZSn27dusmnn8avHVSyZElZuXKlVKvmOvMnMTVqxM8C1XO9fSeuWBE/g6duXe+zK1Iy4qzo7s/SEpffLlknb7z3pU/LyOig9jKOdVz/3pW07xoLcRbC0Z/lq5EjP3FJEvbs+YBJ7AUL/VlAaLlfmTLAIPP11183CzU/88wzUqhQIbn33sTrAV+4cEH69esnEyZMsGvBP/RQcEcFRKqLFy/LtNnL5Z3xX3mtcx1IeczTsedk+66D5ou9fZu6UrdmRSlVooBkyZRRjhw7JX9tP2DWfJn8+VKfRlI5Fyy2ZuqVLFFAOrSpZ0YElb2uqOTIllmOnjhtFhb+7offZerMZbLnH99q9fuiUIFcLqPBtu3wPbDS69BAxSpHUDqm0FXHlHUGbY4RxN5oIKbtlypR0GPbiBwTJsx2ud2wYfwft5bNm3d4/MIPp+3b4zuDVIkSvq/xULx4oatKijodOnRMTp8+E1DbhQvnMwGTVVJq507XtuFdXFqGIsE/xFnJH2c5ZcqYXj5861H7j8fP5iyXud96L+eUGDNS9KeN8u6HX5u1XMKNOAveHD16SoYOmWTf7vJQM59ntlmcHYfWyGp/pL3GdUzqP/8clZKlAlsD6/DhE3LakTS/9v8/Y74oVDiPSyy0a9fBq36fd+7YH1Db2q62v3fPlXJQuxO0jdQXZ+n3/0cffWTfrlChgixcuNDv9d/KlSsnlSpVkg0bNsjBgwdl8eLF0rBhQ4/HO5OILVq0kGhGnBXd/Vn+erRzE8nsmKk3/7ukx2XEWQhXf5YvNJH19dc/upT1fOCBZhJM9GdFr2iLsyS1J/U0oHzppZdMUKPBzf333y+1atWSO+64wwSNlu+//94EkKtWrZLp06fbNdq1VEHfvn3l2mtdRz1Gqw8mf2+2YNLZdJajx07LjPF9pIVjvQdL0cJ5zabJuOd63y3D3p4loyd84/OCxRpgDXuhnbRvU8+M0HUqXCC32WpWKyfP9Gwto8Z/JS+9+bnLiNykBEFO+w/5Xu9e/7A+fPSUqRGuiiaoQZ47Z1aX8gr7D/o2qsly8PAJOwhK2DYig9bgHjZsvPz441r7vptvriB168bX6rZGG2oZAkuRIvFlZ0+dipUFC1bIt9+ukB079pkFf7NnzyrXXltYbrutqtx77x2SP3/uoF3z4cNXSltZChTw/bOVL5/rdRw4cDRobeuIMl0vUAMpdfBg/OgrAKFBnJX8cZbTy889IOVKX1nTYv/B49Jn4MdJblNLbDa5/2VJLsRZ8OblQR/J8WNXZqOWKVtMejzmfQ2vhHI6OjTVyZPxHTK+sJ7fcuhgYOtfqSOHXT+HBRL8DniLhXLnyW7WvDPXccD1Ok6dPGM6ve22fZylZ8mXL6ed1EvKa0TKt27dOpc19MqWLSs//PCDFCgQ2BpfWsLzueeeM/saU+iMvcyZM1913JQpU8xjKm/evNKmTRuJZsRZ0d2f5Y8uDzSQAX3+Z9/WpGEw2ifOQjj6s3w1atRUl5m1vXu393mmm6/ozwJSSFJP9enTR3bv3i2jRo0yQY0GgVYgqLedZQ2U8x8QDRKHDh0azMtJdZyz6WKuLWA2b3Sk0OuDOsmNlWKke7+xHpNvNzjazp4ts3S673avbevIpmd73W2m77d95C05e+6CJEX+vK7rbRz3c0TW8ZNn7CAod65srm0nWMvjWABtW3T0VLasmST2zHm/2kBw6VoIOppt9+4D8uOPa2Tq1K9k375D9uNFixaQd999/qrzjh496TKK3ErSffnlEnnllfFy4oRrZ9LRoyfM9ttvm+SDD2bJE0+0k86d/e/kckevxcmfUgj6OcySJZOcPXvlc3jy5Gkvbbv+TniTM2c2OwjSsgrwEwsBIwDEWZGhbo0K8liX+BI5PZ//0CUOSKmIs5CYuV8slyWLfzP72unz8ivdJH0G//+U1FKdmrA6cuRKHLJl027zb5X1b5g3Wza7ri955kzg5WmPHT3lcjtHgoSjNzlyZLWTegljoWPHktZ29hzxx+tIcn2dWbNenXhB9MdZvXv3losXr5RHy5o1q8ybNy/ghJ7VnpaX3LVrl0kYtmzZ0szI0/X5LFOnTjUz1ywDBgxwm/iLNsRZ0duf5UnatGnMQPXCBXNLnVsrSLf2jaRGtfh1uS5cuCT3PfymHPr/76ykIM5COPqzfLFp03ZZujS+wkjZsjHStGkdCTb6s6JYFMVZKVlQk3rq7bfflurVq5u64gcOxE8nt4KghDW2s2XLJv3795dnn3022JeSquion4L5r15kXksSfDh1kaz7c4ccOnpS8uXJIbWqlzPlBGrfEr8Ie4d765nRUM8NnXJVG/qzq1iu2FX3//TrFhn78Xfy89qtpl53zuxZpHqV0mZk012Nq9rHNb7tRhk/soe0f/SdJL3GbFldy/PojEF/nHEEJdkT/FGsQYtTbBLaNu1ny0wQlMy6dh3osnCuU/361WXQoEevGv2jTpxw7YTJnDmTvPbaR1eVOXBHF/AdPnyCbN26W4YN6+VzB1Vi7Tn525njDIISdnpZ98e3ncXPtjMHpUMt1SIGQoCIs5KXxgvj33zUXmdYyz59vTB+xGxKRpwFTw7sPyqvDZ9q3374kbukQoK1j/xRtXo5+fabVWb/+PHT8tPKDVKrdmWv5/3y00Y5kaCjUjtcA3X2rOuAw6wJPqfeZMmS0eNnNJhtW+2T1Et9cdZPP/0kP/4YXx6taNGiMnLkSJ/P11l9mqhy0uTc+PHj5a677jLJQi3jWaJECbMWXK5cuWTt2rUuM9OaNGkivXr1ktSCOCv6+rMSo9WlBvW7z+1jm/7aa9Y2/unXrRIMxFkIR3+WL8aPn+Vy+9FH70ty35U79GdFsSiJs1K6oCf1VPv27eW+++6Tzz//3JQn0NIEhw4dkpMnT0qWLFlM+YabbrpJ6tevb8o/5Mx59Zc3/JNwzTudFffYMx/I9C+vjCyzaImoWfN/NtuTjzQ3ZTStjqknHmku3yz+TZaujA/ilS4QnDVLJpea2y8MmyqjPvza5TgtB6AdW7rd36qWjHujh2TMmN48dk/zGtLpvt/lkxk/BPwaM2a40pbl0v/XP/aV8/gMCUYWZ0xwOyltq/RBnrYO/+3ff6VkUULZsmUx6+R5WsQ74Rf6F18sknXrttjndunSWu64o5Zce20huXjxkmzcuF1mzPhWvvpqmX3OrFnfm5FTjz/eNkmvQdt38rccQrp06TyuYZP0tq/x2DaA0CLOSj46GrxE8SuzGfb+c1T6DY5fXyylI86COxovDew/wV57rnyFEvJw96StrXVHk+p2Uk+9/eYMqVa9nJnF54n+/TF6lGsnlLLWQwnE1bGQf38aO49PeB3O0puBte36Gb98KfDXiZRr5syZLre3bt1qNl/ddtttVyX1VOPGjU3bXbp0kaNHj5p15HQGYEKtW7c2s/as/oLUgjgruvqzElO8aD639+uSHJrM0++eYCHOQjj6s7zRJWS++y7+90r7rZo0qSWhQH8WkEKSeidOnJAcOXLYX6jp06eXdu3amQ2hF3vmnHw2Z7mUvLaAlCheQJ59abLMmOsaACX09gdfScH8ueTJ7nfZ9w3se680TBAE6YCNKTOXmbZjiueXMR9/e1VCLyENvrQkwFsvd7Hve7733TJ11rKAA6O0Cdbv87e0QmLHJ/xDJaltp2HR0GSlAc7+/e7rYsfGnpX3359uRif16HGfSbw5RyUl7JSxEnrXXVdcPvhgkBQrdqXWvMqUKaPUqHGD2Ro2vFWeeWakff7o0dNMcFS6dODrKiT8XfH3D2otJ2JJGPTpHyqejvW3bav9UIzuilr8GwE/EWclv6YNq0jntvXt248+84Gc+v9ERzQgzoI7n326SH75eaNdCmno8IfN/5OiQaOqElOykOzcccAuqflM3zEywgwIjF8TyBkPvTToI/nj97+veiwp15LwcxXMWCjpcVaC35kAO+9SrSj5N2Lz5s0ha7tFixam/ffff1/mzp0r27dvl9jYWFOGs0aNGtK1a1dp3ry5pBbEWdHbn5WY4kXyur1fPwddH2hgttlf/RyUmI84C+Hoz/Lm88+/c0litW/fXK65JjRJXPqzohj/RkRXUu+FF16Q2bNny7333isPPfSQVKlSJVhNwwc6iiiQsgAvvfm5dPjfbZIvz5XaxrWql5dri+WT3Xvjv0C2bPtHuvUZ43fbYyd9J490aiwVylwp3ak10bVUwo8/b5JA/Jsg2aK1z/2RLl1aj6NntV51ktpOcHzC9hFemlgbM+ZFKVeupKnbfepUrEnOffrpV7J8+ZX1YDSQeffdT+X48VMyYED3RNvLnj2rjBs30CWhl1Dz5vVk9+798vbbU+wgY8yYGfLmm/0Cfh0JRxv5mxB3JigTjn5P+Bn3v+3/XDrUCICA0CLOSl55cmWT90bEry80cdpiWbjMfUmclIo4Cwnt2nlA3h45w77d/dGWUqZs8SS3q506uiZf107D7Y6lJYvWSpuWL0rXbs1NKc68+XLKqZNn5NfVW2Tih1/J5k27zHE33HidS3Iv4WwFfyQ1FnJ+rjVmc8ZCSW47we9jUl4nIk9MTIxPsyy++uqrkF5Hvnz5ZODAgWZL7Yizorc/KzEj3v1Cer0wwcwAzJQxvVxf4Vppe3cdefC+2+1BI1p1qniRfHLHfS/J+SSUfCbOQnL1ZznNmbPIpY+oTZvGEir0ZwGhFbQ6CkuWLDElCXTR5fXr10u4XLhwQU6dOuWyxcVRnsRX585fNCOPnOr7sKaFr6bOXBa0ts+eu7JAuEWDLn9YpUDVhQRTtZPadsLjk7K+B5JOv5Tr1LlZ8ufPbfbz5s1lZtJNmPDSVQHPlCnzZdmyNS7nJtSpUwspXryQ1+fV0px58sSXX/nhh9VJKgulMwGdLlxw/Zx64yxJkDAIypw5Y5I+s7oGh0VHssJPGjQGe0NUS444ixgr3qhXHpLCBa6sW7Frz2EzgjzaEGchYefIgBc+lPP//7OrdH1J6dotfjZEUmly7qVhD7l0+OzZc0iGDPxImjTsK9VufFga1HtCnun7vp3Qq1vvBnm81z0u7WTxc606p0yZMiRayskb5+cwYZyV5LYTHJ9YaVK4QZwFPxFnpc7+rBWrNpu4TpNYOhNvxaot0uv5CdLk/pfl+Mn4NVyrVykt/Z9qk6RrJc5COPqzEvPHH1vNQHRL/fq3mKRhqNCfFcWIs6Irqbd37157v2nTphIuw4cPNzXMndvlU1dKxMA3q3/b5nI7pniBoLW9KohtHzsRH1RZi/f6I4fj+GPHXds6nsS2s2fP4tIJcuLUGb/OR/h06HCXPPTQPR4XCtYa5Qk1bVrH56ClTp0qLqURNm3aHvC1JgywtD1/ONcHTNhWzpxJa9t5fO7coQsEo1aaEGyIaskRZxFjXXFfy1rS5q4a9izsHs+Mk9gzrouzRwPiLDh9POFr+X3dNnuW2NBXuvk98t+bZnfVlDHj+0mx/1+n0hNN/PXsfY+8896Tcvas6+9e/ny5An7+nLmyudyOjfWvtNqZ2PhryZUrq2vbOYPXtpaIcv4OwAfEWfATcVbKFYr+LJ012KX3ey73PdKxsWTN4ppI8AdxFsLRn5WYb7750eX23Xc3kFCiPyuKEWdFhKD9ZZY1a/wfMqGqx+vO888/bxYsdm7pclQM2/NHg0NHTrrczvv/pQsire2Dh0+43M6fN4df5xfIFz+D6vBR1+s6eOSkS03mfElo++jx037XMEd4PfbY/S4jqNes2WB3Ejln2intwPJnXTwtkeB06NCxgK8zX4KOKi2t4KsTJ0671EpP+Lry5cudoG3X3wlvjhw54bFtAMGXHHEWMZZI4YK55a2h8esDj5+ySH5Y4ftaLSkJcRYsW7fskTHvfWHffqzXPVLquiIhea5bbq0gX8wbLoNe7iJ1b7tRChS8Mio9S5ZMUqFiCen6cHP58utXpVuPliYmO5zwc1og8KSelvh0On7stF/nH3V8znMn+EznzZfDpZTT8eOBt63Jx2AnVAG4Is5KuULVn/XtknVmJp8lR/YsprxnoIizEI7+rMQsWbLa3s+aNbPUrh3aMsP0ZwGhFbTi/Pfdd5+MHj3a7E+ePFl69+4t4ZAxY0azOaVJE76kYjRIWDvY3/Iw/rSdsEyAP3bvO2JGDVl/1BYtlMfnc3WkkgZhlp17Drs8ruUWDhw6IUX+v01/2jbHF44/Xss3ILLpbLybbiovP/98ZT0k/Vzt2vWPVKhQSnLnzmECHGtUkM6+86e+do4criO1tf55oIoVcy35eeCAb2sDuDs2YVtFixYwa9pYwf+BA0f9GtXkHDWV2FqD8ICFhZEC4ixiLJGGda836+lZundqbDZ/fDfDda0iXZMl0PWFQ4k4C5ZF3//q0pHy9pszzOaPZo2fdrn99fevS9Gi7mfkpc+QTu5pc5vZvNmyebe9r5/VkqUKS6CKFMlrZsFZnZcHDx73+VydeXfGMWM34WvTMlH58ueUw4eudBodPOB72wmP9/S+IRHEWfATcVbKFcr+LF0/ufYt8Ym8cqWLyPdLfw+oLeIshKM/y5M9ew7Ijh377Nv16lUNeWlv+rOiWAqIs6ZPny5t27aVEiVKyM6dO30659y5c/LJJ5/IvHnzZN26dXLkyBFTnrVQoUJSu3Zt6dChgzRq1Mjna9Cy3u+//77Mnz9ftm7dKpcuXZKiRYvKLbfcYtbvbdiwYWTM1HvllVfMC9QFn59++mkZNWqUqRGO8MicKYNZELj6TdfJrTeX8evchCOEjiQYpaold4oVziM331BK6tWsmKS2j/o5AjZhoPL3zgP27XKli/p8brkEo4s3bNlz1THO+8qUKuxzIkdfo7PDz13bCC1dGNrf+twJRw2dPx9/vnNmnn7Znzvne5m1hCOkMmcOfK2X4sULutQK//tv3z9b27fHl5BRZcq4zjbUAO7aawsHqe0SPp8LIDDEWQg14iykBH/+EV/WvERMIcmY0XXtOn9oLFT82viOnB3b//H53B3b49ekUaXLXP37UrpMMXt/184DLrMoEnP0qK6tdSbRtgEEF3FW9PZnOWVJsA6Xv7MAE66X6g/iLISrP8udn35yTUbrenqhRn8WksuBAwf8HpyzdOlSKV26tPTo0UO++uor2bdvn4kDYmNjZdu2bTJp0iRp3LixNGvWTI4f9z5Yb8GCBVKhQgUZMmSIrFmzRk6fPi3nz5+Xv//+W6ZNm2aSgx07dpQzZ84k/0w9zTi+/vrrJhjSDORTTz1lygncfPPNUrFiRcmdO7dkyeLbWgADB7qOaEbiNDA5vGmiGaWg9v5zVMrU6Onz+VVvvM7l9rr1rhnsv34ebU/H10CkQKWuPi9CWu2qtndIUqxet03K/n9Ao4FKjuyZzYLG3ujCxomt9Wfa/m2bNL7tRrOfLWsmqVCmqGzcujcobSP4du78RwYNek/27j1oRvF06dJa+vXr7PP5JxLUnc+bN37K/Y03lpPff99i3/7rr91yww1lfWp37974QN0KZAKlpV8qVSotv/56pdTb+vXbzB+avgTougiyk47kSujGG8vKzp377Pfz9Okzkj2760xDd5zvzZW2y3k9BylvZBMiC3EWwoE4C+GksymOH4+VggVdSyh5sm/fYVMa1FKjZqUkX8P1N5QyCTel/z99+qxP69f9+cffLrdvSPA3j9X2TyvWm/1z5y7I33//I2Ucib6ktA0viLPgJ+Ks6O3Pmv5BHyldqrCUKJZPNmzeI7e19v3n40x2qcMJknz+Is5COPqz3Fm71nW9zho1bpBQoz8rikVwnBUbGyutWrUys+T8SejdcccdcvHiRfuzW6NGDZPk08TeL7/8Ijt2XMlnfPPNN3L77bfLypUrXUp3Oy1btsxcg9WerpnboEEDyZYtm/z666+yadOVqj1Tpkwx1zt79my/KsQFPalXrVo1+wL0//qLqtMW9UXq5g+CIP+cPXdBduw+JNfFXJmOXKxIXilfpqhs/it+anViC8+3uKOqfVuTdj/96voP3PrNu6VBnevtWXu316pk6ov74u5mt7rcXvpT0hZ+XrjsT2nfpp7Z17IFd9x+o8yc97PX85o2rOLyfq101Ea3LPrxT3nhyTYu5/gSBDnbvtLOlSnwCC1dKNcqN6CWLFnlcxCkJaWcX+RZsmSWQoXyuZQi+OSTuS4LCvua1HNek7ab1FE/WufcCoK0BrkGN5p09OaHH1a7lCaIiSnitu0vv1xi9nX0+I8/rpVmzer61baOVqxalbUf/BUXuTEQIhRxVvKYMnOZ2fzR4d56Mn7koxFfbtMd4iyoR3vebTZ/jBk9R8a+/6VP5TanTv5exoyeLaf/vyNz0dK3JV9+72vjzZ/r+m/d7Q2SvhZMzVqV7Xa1DOeK5X/KnU1d/35xZ5mj/JrGQlVuLuu27Q/GxMeTPy793aek3rIfXEfT16iV9ORlakOcBX8RZ0Vvf1bJEgWlYtkr//Zq9alCBXKZUpW+SLiG3pZt3q8pMcRZCEd/lju//bbFpX+oYMG8Eg70Z0WnSI2zjhw5Iq1bt5ZVq1b5fM7Zs2elXbt2dgKuatWq8umnn0rZsvGxvcYEU6dONbP4dGbdH3/8IX379pWxY8e6bU/LdFrttWnTRiZOnCg5csTPKte2Hn74YTNz74svvpBx48aZtv0V1BW39UVam7v7fNkQmG8W/eZyu3unO3w676F2DaRo4fh/zGfO/0lOnjoblLbvbFDFZdSPLjLsS2CWmK8XrpEzjvKGPR5s4vUcrXtuJSXVF9+skvNuZhr+9OtW2bMvvm5z13YNJX36xGva582dXe5rWcvlNe7e63udaCQtCKpUKX5U3rZte2TlSt+SzTNmfGtG8Vjq1bvZpZ64jlrKnz++rvznn38vhw4d89ruihW/mVl9lvr1qye5TnnTpnVcbk+ePN/rORrMOOult2xZ3+1xDRrc4lIOYcoU721rWYOVK+M7m+64o5ZZdxDRR2uQa6dGTEyMz+do54cGJHfddZcUK1ZMMmXKJNmzZ5cyZcpI586dZeHChX5dg46uGjx4sOlo0SAoc+bMZrSUBl2LFi2S1IY4C6FGnIVwKFosn53QU4sXrfV6jpajnPrJd/ZtXUvvllsrJPlabqt/k2TKHF9O7bOp3r9btv/9j/zy05UOKtWwcVW3JdluqlJaCjnWNZr1+Q9y6WL8WoXuHD9+WhZ8Hd/Bq8lC1tQDwoM4Kzr7sxb/+KdLEtDXtiuVK26SbhZNBP6yNmmz2IizEI7+LHdLxOiae5ayZcNXbpL+LITL2rVrTb/RihUr/Dpv/Pjx8s8/V34/dP097bNyJvSU9otpok7X27NMmDBB9uy5uuyrrs9r3V+lShVTatOZ0FPt27c351uGDh0aUMnvoM3UGzRoULCaQgA+mrZYHu3cxF50V4ObmfN+Ml/Knmi98lf6t3cZ1fTm+/GjSS2fzVkug/rdZ6bwWyN57m9VS6Z/6XnEWqkSBWXMa4+43PfqqDmSVFqaYOqsH+WRjo3NbV20+MlHmsvbH3zlsTb7hLcft98X9f7EBW6P1SB83CffydDn29mv4bUBHeWpgR+7PV5/qT8Y2cNlweL3Jn6TpNcH/7Rt21QGDLiyoLl66aWx8vnnbyY65f7PP/+SN9742OXn+PDD8SPaVPr06aRLl1by2msfmdsaML3wwjvy/vsvegyWjh076XIt6sEHW0pSlSxZVOrWvdkENmr+/KXSsOGtVwVHlqNHT5gyDhZda6Zt2zvdHqvvU6tWDeSzz658btes2SgTJsyWhx66x+3xurbg00+PdFkTpmPHFkl6falWBJcrSEoNck22WQGRRYMTrUFu1SFv2rSpGZmkZYy81SDXYOfYMdeEutYgt+qQa2Clo6M8lT2IJsRZCAfiLISDls3Mli2zxMZeSex9PPEbadGqtkvHjNO///4nzz89Tk6ejO/AeqRHy4DK1CSkpTZbtKwtn0+/MtL7t7VbZdLEb+TBrk3dHq9lNPs/94GZ1Wdp1+HK70tCWkru/nYN5Z2Rn5vbe/ccltdfmyYvvNjR7fEaXw144UM5cya+w7f9//8uIrriLEQe4qzo7c+aOG2x9Hyoqd32E480lzlf/yJ/bNzlse3cObPK5Pd7u8Q3b42dl+TELXEWwtGfldBff+1y+eyWKlVcwoX+rCgVQXHW2bNn5dVXXzUltHXmm9Jy2Xq/L2bMmGHvv/DCC5Irl+fqIffcc49Ur15dVq9eLZcvX5avv/5aunfv7nKMDnJ3xhbp07vvQ9a+szFjxsjy5cvN+n1a+ltn9fmDpF6U0Gn1Ez5dZAcHmpSY/dHT0vuFCW6Tbx3/d5u8OeRBl4WCR4z+wu30fF1o+NVRs+3gQI17o4fky5tDxk76zvyh7dS8cVUZPfxhU9bA8smMH2ThMs/T+DevGCUliuf3qVTViFFz5P5WtSVnjivBh16X/kP/+ntfuPyBXaRgbpk0updUvaGUfd+02ctlzR/bPV7HmI+/M+/htcWuXEuPzldGTj03bKrLOoL63GNfe0SaNbzZvk8Dzjlf+z7FF0nXpk0j+eyzBbJhw5URczqap1OnF+S11/pcVfZSFx+eNWuhDB/+oRmpZOnQoblcf/3Vi3Hrl7tO5d+y5UpNfg1COnd+UYYO7SWlSrmWTtq48W/p2/cN2bcvvmbzXXfdlmhZgXLlXIOHRYs+lGLF3K+/16dPJ7Ow8eXL/5pg7Nln35LY2DPyv/81uWrU0RNPvOpyHVqbPbFSDI89dr8JrGJjr3zhvfHGJPNZ7979XlNH2nLw4FHp0+d1+71WLVrc7nNZUiQQhI7IUElNNchTEuIsJAVxFiKJzmr7X9v68tGHX5vb+/Yelid7jpLhr3eXPHlcR7Lu3XNIBvafIGsc5dRq1q4sze6qmehzNG3UV/7556h9+8OPn5Xqt7if2detR0v55quf7STj2yNnyIWLl+Shbne5dKQePHhcnus3RjZuiF+vSa+j8vXxvwMJtW3XSGZMWyz791+5lumfXpkJ2Pfp+83vlXMm4uABE02JTucsvcZNqif6OuFBlMcECD7irOjtz9r69z8y5uNvTWLPSpTNn/qCPPrMB/LV92uuOr5ujQpmgLpVDlRpbPP+x996vH7iLERaf5aTrjfnFIzSm/RnpXIREmdt27ZN6tevL3v3xv/bX758eXnvvfekYcOGXs/X3yvtX7LoAHRv6tSpY5J6yuqXsqxfv162b7/yb7T2XTVr1izRttq2bWuSemrWrFnJl9RD8nvu5SlyQ4USUqPalX+UdMTNx+/2MnW1dS27Y8dPS8H8uaRBncr2l7xl8udLZfg7sz22PXLsfKl243XS+v/XyMuYMb28MfhBefKRu0yy7uDhE5Ind3ape2sFU//cSR/v3X9i0F7nPwePy2PPfiCfjL4yckq3wU/fZ0ZzffvDOjlx8oyUKVlYmmg5HUcpnL93HpAnByR+HVqfvHPv0SbIswJEDYTatKgp3yxaKwcPn5Rri+aTZo1uluzZMrskPrs+ET+aBOGhX9LvvPOstG//nPmSVhs3bpcWLXpJtWoVzaK8GiDrwsMaRCQsodmgwa3y/PMPu21bZ+TpzLwHH+xvFi+2Rv40a/aY3HLL9aZkgZbv2LDhb1Mj3Dna57rrisuQIY8F7XVWrHidqa/+6qtXpmdfuHBRXnxxtEyYMEdq1rzRlAvQ0VcrVqxzuY4qVcpLz54PJNq2BnSaqNQAR8/V7Z13psiMGQvM2oI5cmQzQeDSpb/KxYvxfwhce21hGTTI/5rPiGyprQY5gKsRZyEcHunRSpYsWis7dxwwt3/+aYM0bdxP6ta9Qa4tUUjOnDlnylz+unqzSyenlt187Y34NSuDoWDB3DLopS7ybL8x5rl0e2/UbJk14wepXfcGyZEzi+zaeVCWL/vdzASxFC9eQF4Y4H7WnSVLlowy/PUe0uNhHT180U7sfffNKql7242SL19Ok/BbumSdSyddrlzZ5JURrlVPACCahbI/68VXp0nl8tfK7bWvrFGaP28OmTmhn0kCLv9lkxw/EWv6s2pWK2uOc9q+66Dc0/k1k5AIBuIshKM/y2n//sMut/PnT7xiTrDRn4VQ2bt3r53Q00Hejz/+uIwYMcLnAeqnT582s++0Da04pUvIeJMuXTqX852sBJ2qXbu2x1l6zgShJZDlZUKW1NNs548//ihfffWVbNiwQQ4ePCinTp2S/PnzS6FChUyd07vvvlsqVEj6Wgi44tz5i9K68wgZP/JRaXFHNfv+stcVMZs7Osvu/Y8WyLMvT0m0lIA+9mDv0TLyRKw81C4+262LGHdu676+sZr+xQrp8fQ4l1FBwTD7q18kY8YxMmbEIybBqIoXzScPt2/k9nhd0LhFh+Gm3IE3Wov83ofekM/GPWWXItCgr9N9t7s9fv+h49Kq46uy21G/HOFTvHghmTz5FXnyyREmALI+r6tXbzCbO/qPvU7Jf+qpji6jdxLSkUaffjpCHntsqKxfv81u+5df/jCbOzo7b8yYFyVbtvgyFsGgI5QuXbokb701xQ50dCSXs9a4U5UqFeSDDwaaUY7eaOmDK4HVu2bRZbV//xGZPv1bjyUUJk58KdGyEEg55QqcNcg1oNm1y3MpGl9rkCcsWWDVINcyCNboI60h3r9/fylevLjXGuQJgyEty6lt6v+tGuRdunSRjBlTTz184iyEEnEWQk2TXeM+fFoe7fambN9+5Tvk/LmL8v138aNlE6p+awV5463HJUfO4Mcfd9x5i5mdN2TAR3YsdODAMbMOnjsxJQvJ2PFPm/Kd3lS5uYy8M/oJ6fPEu3ZpTV07b+4X8X/4O2mi7/0P+kqRop5HpiPlxVlIWYizoqs/S/uj2nR9Xd4f0U3ub13bvr9i2WJm8+S7H343ya6jx107bpOKOAvh6M9yLhXjlCXLlaWVwon+rCgTYXFWnTp1ZOTIkaY0pj+030r7m/zx22/xa8AWLOg6O1XjBeeMQW/KlStnfp/1d14TkRprJGwzMfH1RIJI35CSJUuaqY76pn777beybt06MwVRS3B9+eWXMmDAAKlcubIZ0b948eJQXEaqpIsC3/fwm9LhsXcSnZavo0wXLP5Nbm89UJ55abJPtcH1nJ7PfSjN278iP6xwnZmUMLDS0U4tO75qRgm5W8Q3GLT0QPUmz8pXC9fY/3AndPxkrLz+3pdSs9kLssdRgsebJcvXy00N+pnn0ODSHV3g+IPJ30vVhk/Ln5t2B/w6kHQlShSR6dPfkP79u3mc8q80ILjjjpoya9ZIefrpzmamnTc68kfrmuvMu4RlN52KFi0gL774iEkC5s3ruQZzUjzyyP9k+vTXpXr1Sh5LDebPn0eefrqLTJky3IxK8lXr1g1k7tx3pX796h7flxw5ssojj9wrc+a8LUWKFAj4dSCy6My4gQMHmpFEVkJPk2++CqQGubJqkCfkTw1ya2STVYM8tSDOQjgQZyHUChXOK9NmDpZu3VtIzkQSdWXLFZehw7vJ+InPmBlsoaJr68384mWpd/uNHmOh7DmySNeHm8tnM4dI4SK+l6+qUauSfPHVcFOu0+rATShT5gzyv/vry+y5w6RcgpkiAMKHOCs6+7OsWWya8PrJUdI5Ie3n+mXNVrm/25vSqtOrQU/oWYizEI7+LOWsBKCc5b/Dif4sBFuJEiXMUjA6CMffhF4gtL9syZIr63CrW265xeXxnTvjS/THxMR4bS9TpkySL1/8ID5/B9iniUvqSq8JvvweeOABmTlzprnta9O6iLh23GlgFAyZr018em5qonW4tXyBrm+XNUsmE5DsP3hcVq7eYgKmpMj7/+UJihbOY0YAaZmAA4dOyKrf/jLT+sMpT65spvZ5kUJ5JFvWTOa1aSmF1eu2JXmWYNYsGaVezYpSvEg+yZUzq5yOPWfqsv+y9i+JdSxkn1Kc2z1Eop3W4tYFhHVEkgbIuXJlN8HRzTdXkMyZMyW5bV1D78iRE2b6fp48OaVixVKmpEA41/TS8gxaDlRLMOiopJw5s0n58iWlcuUyPgd3nhw/fkpWr15vnkPLX+kIpjJlrjWzEJMrAAyv0NdVL/W45/Iwgdr+nvsFoZNag1wDJWdwknAUsyYArVKZu3fvvmrmXUJ9+vSRt956y+w/8cQT8vbbb7vUIL/++uvtGuTHjh1LtGSBXmfPnj3NvsYfWvYzmkVCnEWMlToRZ/nu+I7eyX0JKdKli5dl/fod8tfWPXLq5BnTcZUvf06zXl0Jx7pG4XLiRKwp/Xno4HE5e+a8SeZdV7qoXH9DqSTHQtre6tWb5cD+o+a1Zs2WWWJKFpYbb7pOsmaNL4cWrTJdk/h6iNEUZyFlIc5KXf1Z2qb2ZxUumFuyZc0sJ0+dsdvWcpThRJzlO/qzwj/LLhTozwqV8KwRGOlx1s6dO83gHG/9Wf66//777UHtujyMDi7XfivLrbfeai9lo31TGlN4U7FiRXttPq0O4G0dvpCV33z00Ufl888/t6cO5smTxx6VX7hwYdPxp/VGtfNQFxXUdXC0hIEGT4MHD5a8efPKY48Fbx0qXKnXrVP7Q0EDqvluFhZODsdOxMqXC64sVBlsZ85ekG8WxU+vReTTNe10S2lt+0NnEDZrVjckbefOnUPuuKNWSNpGZC0snNprkKc0xFlILsRZCLX0GdKZMpW6RQKdEdiocXz5t2DKkjWT3Hb7TSFpG5EVZyFlIc5KXf1ZOiB9zte+ryMeSsRZiMQ+p1CiPyuFS4Vx1kcffeRSpUoHqzsTegn7tzJn9m2gXtasWT32j4UtqadTHXVdHSsAeuqpp2TYsGFmKqEnZ86ckaefflrGjh1rznnmmWekdevWUqSI+3rZAAAg+FJrDfKUhDgLAAAgNIizAABIPhcuXDCbU8aMGc2W3BYuXGgG/lh0PV39/k/Ief2JxQ9OzoHsuu6kP4K2pp5zDZznn39e3nzzTa8vQLOR77//vvTq1cvcPnfunCmlBQBA1C8sHOwtAKm9BnlKQpwFAEDKirOQchBnAQCQfHHW8OHDJWfOnC6b3pfcli5dagbsWAk7/e7XGXs6ez+ha665xu1+qAQtqWeVzNIR8i+99JJf52qpLy1tYNUPBQAAoad1xuvVqxe259MRzLoOn1WDvFGjRi6PO8t++jrjzpnUO3LkiEQr4iwAAIDQIM4CACD56ICakydPumx6X3JasGCBNG3a1MzMt2bVTZ8+XSpXruy1lObly5d9eg7n7Dx/ZyUGLal34MABU6rgtttu8zsbqSOg6tevb0oW/PXXX8G6JAAAIlKclvYJ8hbpIrEGeUpCnAUAgG9SY5yFpCHOAgAg+eIsTWjpwG/nlpylNydNmiQtW7Y0s/BVunTpzNIzzZs393iOs38rNjbWp+exEoYqe/bsybOmnr7ZR48eNS8yEFannLOWKAAAUSloQ2riUYPc/xrkKQlxFgAAyRdnIboRZwEA4KMoj7OGDRsmL774on07Q4YMZoaeluFMjDVrX2lM4QtntakCBQokz49BO+h0ZJKuyxOI33//3YyMKl++fLAuCQCAVIMa5NGNOAsAACA0iLMAAEjd/vvvP+nRo4dLQk9n32lpbW8JPeWMAfbs2eP1+PPnz7sk9WJiYpInqdelSxf7onVRYX9rlGoQpDp06BCsSwIAINUsLEwNcv9rkKckxFkAACRfnIXoRpwFAEDqjbP+/fdf6dixo4wbN86+L3/+/LJkyRJp1KiRT21UrFjR3t+0aZPX4zdv3mwGFKnChQu7zPQLa1Kvc+fO0qxZM3Mxzz77rLz99ts+nffTTz9J+/btzX6VKlVcynMBAADfUIPc/xrkKQlxFgAAQGgQZwEAkHp169ZNPv30U/t2yZIlZeXKlVKtWjWf26hRo4a9r+daCTtPVqxYYe/XrVvX72sO2pp6aubMmfLQQw+ZTru+ffvK5MmT5eG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      "text/plain": [
       "<Figure size 2200x1200 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.model_selection import KFold\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# RR_only\n",
    "accuracy_score_list=[]\n",
    "confusion_matrix_list=[]\n",
    "\n",
    "X_pre=train_data[:,2:-4]\n",
    "shape1=train_data.shape[0]\n",
    "y_1=np.zeros(shape1)\n",
    "data_pre=np.concatenate((y_1.reshape(-1,1), X_pre), axis=1)\n",
    "X_post=test_data[:,2:-4]\n",
    "shape2=test_data.shape[0]\n",
    "y_2=np.ones(shape2)\n",
    "data_post=np.concatenate((y_2.reshape(-1,1), X_post), axis=1)\n",
    "np.random.shuffle(data_pre)\n",
    "np.random.shuffle(data_post)\n",
    "# 5-fold cross-validation\n",
    "kf_pre=KFold(n_splits=5)\n",
    "kf_pre.get_n_splits(data_pre)\n",
    "\n",
    "kf_post=KFold(n_splits=5)\n",
    "kf_post.get_n_splits(data_post)\n",
    "\n",
    "for (train_index_pre, test_index_pre), (train_index_post, test_index_post) in zip(kf_pre.split(data_pre), kf_post.split(data_post)):\n",
    "    # # get training set\n",
    "    # print(train_index_pre)\n",
    "    # print(test_index_pre)\n",
    "    train_data_pre=data_pre[train_index_pre, :]\n",
    "    train_data_post=data_post[train_index_post, :]\n",
    "    train_set=np.concatenate((train_data_post, train_data_pre), axis=0)\n",
    "    # get test set\n",
    "    test_data_pre=data_pre[test_index_pre, :]\n",
    "    test_data_post=data_post[test_index_post, :]\n",
    "    test_set=np.concatenate((test_data_post, test_data_pre), axis=0)\n",
    "    \n",
    "    X_train = train_set[:, 1:]\n",
    "    Y_train = train_set[:, 0]\n",
    "    X_test = test_set[:, 1:]\n",
    "    Y_test = test_set[:, 0]\n",
    "\n",
    "    sc=StandardScaler()\n",
    "    X_train_norm = sc.fit_transform(X_train)\n",
    "    X_test_norm = sc.transform(X_test)\n",
    "\n",
    "    svm=LinearSVC(penalty='l1', C=1.0).fit(X_train_norm, Y_train)\n",
    "    Y_pred = svm.predict(X_test_norm)\n",
    "    acc=accuracy_score(Y_test, Y_pred)\n",
    "    cm=confusion_matrix(Y_test, Y_pred)\n",
    "    print(acc)\n",
    "    print(cm)\n",
    "    accuracy_score_list.append(acc)\n",
    "    confusion_matrix_list.append(cm)\n",
    "classes=['pre','post']\n",
    "fig, axes = plt.subplots(2, 3, figsize=(22,12))\n",
    "axes=axes.flatten()\n",
    "\n",
    "for i in range(5):\n",
    "    plt.rcParams['font.family']='arial'\n",
    "    plt.rcParams['font.size']=25\n",
    "    sns.heatmap(confusion_matrix_list[i], ax=axes[i], annot=True, cmap='YlGnBu', fmt='.2f', xticklabels=classes, yticklabels=classes, annot_kws={'size': 30})\n",
    "    axes[i].set_title(f'Fold{i+1} ACC: {\"{:.2f}\".format(accuracy_score_list[i]*100)}%', fontsize=25)\n",
    "\n",
    "axes[5].axis('off')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "7c74c254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold=0.5\n",
      "0.8117770767613038\n",
      "0.5995500562429696\n",
      "[[440 110]\n",
      " [246  93]]\n",
      "threshold=0.6\n",
      "0.6006749156355455\n",
      "[[403 147]\n",
      " [208 131]]\n",
      "threshold=0.7\n",
      "0.5804274465691789\n",
      "[[276 274]\n",
      " [ 99 240]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Long\\.conda\\envs\\newev\\Lib\\site-packages\\sklearn\\svm\\_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "# data_split\n",
    "Y_train = train_data[:,1]\n",
    "Y_test = test_data[:,1]\n",
    "X_train = train_data[:, 2:-1]\n",
    "X_test = test_data[:, 2:-1]\n",
    "event_test = test_data[:, 0]\n",
    "# datastandard\n",
    "sc = StandardScaler()\n",
    "X_train_norm=sc.fit_transform(X_train)\n",
    "X_test_norm=sc.transform(X_test)\n",
    "# l1 svm\n",
    "linear_svm = LinearSVC(penalty='l1',  C=1.0)\n",
    "linear_svm = CalibratedClassifierCV(linear_svm)\n",
    "linear_svm.fit(X_train, Y_train)\n",
    "print('threshold=0.5')\n",
    "print(accuracy_score(Y_train,linear_svm.predict(X_train)))\n",
    "print(accuracy_score(Y_test, linear_svm.predict(X_test)))\n",
    "print(confusion_matrix(Y_test, linear_svm.predict(X_test)))\n",
    "Y_predict = linear_svm.predict(X_test)\n",
    "Y_proba = linear_svm.predict_proba(X_test)\n",
    "Y_predict_norm = linear_svm.predict(X_test_norm)\n",
    "Y_predict = Y_predict.reshape(-1,1)\n",
    "Y_predict_norm = Y_predict_norm.reshape(-1,1)\n",
    "Y_output=np.concatenate((event_test.reshape(-1,1),Y_test.reshape(-1,1), Y_predict, Y_proba, Y_predict_norm), axis=1)\n",
    "\n",
    "mask=(Y_proba[:, 0]>0.5)&(Y_proba[:,0]<0.6)\n",
    "Y_predict_1=np.copy(Y_predict)\n",
    "Y_predict_1[mask] = 1\n",
    "print('threshold=0.6')\n",
    "print(accuracy_score(Y_test, Y_predict_1))\n",
    "print(confusion_matrix(Y_test, Y_predict_1))\n",
    "\n",
    "mask=(Y_proba[:, 0]>0.5)&(Y_proba[:,0]<0.7)\n",
    "Y_predict_2=np.copy(Y_predict)\n",
    "Y_predict_2[mask] = 1\n",
    "print('threshold=0.7')\n",
    "print(accuracy_score(Y_test, Y_predict_2))\n",
    "print(confusion_matrix(Y_test, Y_predict_2))\n",
    "\n",
    "Y_output2=np.concatenate((event_test.reshape(-1,1), Y_test.reshape(-1,1), Y_predict, Y_predict_1, Y_predict_2), axis=1)\n",
    "# np.savetxt(r'C:\\Users\\Long\\Desktop\\PPG_model\\Results\\jiangshengdian.txt', Y_output2)\n",
    "# # # rbf kernel svm\n",
    "# fs_svc = LinearSVC(penalty='l1', C=0.1).fit(X_train_norm,Y_train)\n",
    "# fs_model = SelectFromModel(fs_svc, prefit=True)\n",
    "# weights = fs_svc.coef_.flatten()\n",
    "# X_weight_train = X_train_norm * weights\n",
    "# X_weight_test = X_test_norm * weights\n",
    "# rf = RandomForestClassifier(n_estimators=1000, random_state=42).fit(X_weight_train, Y_train)\n",
    "# print(accuracy_score(Y_test, rf.predict(X_weight_test)))\n",
    "# print(confusion_matrix(Y_test, rf.predict(X_weight_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "09d44695",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from xgboost import XGBClassifier\n",
    "# xgb_model = XGBClassifier(\n",
    "#     reg_alpha=1.0,   # 启用L1正则化\n",
    "#     reg_lambda=0.0,  # 如果只想使用L1，可以关闭L2\n",
    "#     max_depth=20,\n",
    "#     learning_rate=0.1,\n",
    "#     n_estimators=10000,\n",
    "#     use_label_encoder=False,\n",
    "#     eval_metric='logloss'\n",
    "# )\n",
    "# xgb_model.fit(X_train_norm, Y_train)\n",
    "# print(confusion_matrix(Y_test,xgb_model.predict(X_test_norm)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "fdb8020a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df=pd.read_csv(r\"D:\\multimode_database\\sub04-chenhaowei\\watch\\ppg_green_raw\\250710\\10.csv\",header=None)\n",
    "# aligned_df=pd.read_csv(r\"D:\\multimode_database\\sub04-chenhaowei\\watch\\ppg_green_raw\\250710\\aligned_10.csv\")\n",
    "# # imu_df=pd.read_csv(r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/imu_raw/250709/17.csv\",header=None)\n",
    "# # imu_df1=pd.read_csv(r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/imu_raw/250709/aligned_15.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "a6f1ecd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(12, 6))\n",
    "# plt.plot(df.iloc[:, 0], df.iloc[:, 1], label='Original PPG')\n",
    "# # plt.show()\n",
    "# plt.plot(aligned_df.iloc[:,0], aligned_df.iloc[:, 1], label='Aligned PPG', linestyle='--')\n",
    "\n",
    "# # plt.plot(imu_df1.iloc[:, 0], imu_df1.iloc[:, 1], label='IMU Data', linestyle=':')"
   ]
  }
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